Make your library legible to machines without making it cryptic to people. The reliable path is to establish one canonical project identity, put a task-oriented quick start at the top of the README, publish explicit compatibility and licensing evidence, expose accurate machine-readable interfaces, and test the complete agent journey from discovery through recovery.
No single file makes a package appear in every AI-generated answer. llms.txt, API catalogs, registries, and MCP servers are useful discovery or execution surfaces, but they work only when their names, versions, links, schemas, ownership and permissions agree with the library that users actually install.
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What AI-agent discoverability actually means
Discoverability is more than search ranking. ReadMe describes it as how easily AI agents and assistants can find, crawl and understand documentation. An agent must identify the right project, distinguish it from similarly named packages, choose a supported release, install it in the current environment, complete a normal task and recover when something goes wrong.
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- Identity: the project name, package coordinate, repository and documentation site must point to the same software.
- Meaning: the first documentation screen must explain what the library does, who should use it and when it is the wrong choice.
- Execution: commands, examples, parameters, return values and errors must be complete enough to run without guessing.
- Trust: license, maintainers, release history, security contact, provenance and compatibility claims must be inspectable.
- Interfaces: APIs and tools need machine-readable descriptions that match the implementation.
Think of each surface as a contract. A short README is not a substitute for an API schema, and an MCP listing is not proof that a package is safe. The objective is a consistent evidence trail that both a person and an automated agent can follow.
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Establish one canonical identity before adding new files
Agents often encounter a package page, a source repository, a documentation host, a registry record and a release archive in separate searches. Make every one of those surfaces agree.
Choose the identifiers that never drift
- Use one project name and the exact package-manager name everywhere.
- Point every official page to one canonical repository URL and one canonical documentation URL.
- Publish a standard license identifier, not only a prose description.
- Use one versioning scheme and explain whether releases follow semantic versioning or another policy.
- Keep the repository description, package metadata, README title and registry listing aligned.
Add a compact section near the beginning of the README with four answers: what the library is, who it is for, what it is not for, and which release line is currently supported. This prevents an agent from selecting a similarly named utility, an abandoned fork or an incompatible major version.
Make the first screen decisive
The opening screen should contain the project purpose, supported runtimes and platforms, the installation instruction, and the smallest successful example. Put badges, historical background and long feature inventories below that path. A coding agent usually has limited context; burying the first working route under branding or exhaustive API lists increases the chance of a wrong assumption.
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Write a README around tasks, not features
A feature catalog tells readers what exists. A task-shaped README tells an agent what to do next. Organize the main path around jobs that users actually perform.
Recommended top-level sequence
- Install: show the exact package-manager command and any operating-system, runtime or compiler prerequisite.
- Configure: identify required environment variables, credentials, files, network access and safe defaults.
- Perform the common operation: provide a complete, minimal example that can be copied without filling hidden gaps.
- Handle failures: list likely errors, their causes and the corrective action.
- Test locally: explain how to run the project’s tests or a supported smoke check without contacting production services.
- Upgrade: link to the changelog, deprecation policy and migration notes for breaking releases.
- Troubleshoot: provide diagnostic commands, expected output and a route to report unresolved problems.
Each task should name the language and runtime version used in its example. If an operation differs on Windows, macOS, Linux, browsers or serverless platforms, label those branches instead of implying portability. A complete minimal example includes imports, initialization, inputs, the call, output handling and cleanup where relevant.
Connect conceptual, reference and runnable documentation
Conceptual pages should link to the exact API reference entry they rely on. Reference pages should link back to a runnable example. Keep parameter names, option names and return types identical across prose, code, generated schemas and the implementation. If a name changed, show the current name first and mark the old spelling as deprecated rather than leaving both equally prominent.
Document failure modes as first-class tasks
Agents often stop at the first exception. For every common failure, state the observable symptom, the probable cause, the check to run and the safe fix. Distinguish authentication failures from authorization failures, malformed input from unsupported versions, and transient network errors from permanent configuration errors. Never advise disabling verification, widening permissions or retrying a destructive operation without describing the risk.
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Build a documentation structure machines can crawl
Serve clean Markdown wherever practical. Avoid forcing an agent to infer content from a visually complex navigation shell, client-rendered page or image-only diagram. Stable headings, descriptive links and predictable paths make both ordinary crawling and retrieval-based systems more reliable.
Create a focused project index
A root llms.txt or equivalent index can state the project’s purpose and link to the canonical getting-started guide, API reference, security page, compatibility matrix, changelog and troubleshooting guide. Keep the index short: it is a map, not a second README. ReadMe documents both section-level and category-level indexes for larger documentation sets; use that pattern when a single page cannot describe the whole site.
Do not claim that the presence of llms.txt guarantees inclusion in an AI answer. It improves navigation only when crawlers can reach it and the linked pages are accurate. Check that every link resolves, that redirects preserve the canonical destination and that old release documentation is clearly labeled.
Expose stable formats and metadata
- Provide Markdown or other text-first representations for important guides.
- Use descriptive page titles and headings that include the operation or API concept being explained.
- Include version information on every versioned documentation set.
- Mark deprecated pages and point to their replacements.
- Keep navigation links consistent so an agent can move from installation to reference to troubleshooting without guessing.
Machine readability should mirror the human information architecture. A separate index that points to stale or contradictory pages creates more confusion than no index at all.
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Different surfaces solve different problems. Add the least powerful surface that fully describes the library, then maintain it as part of the release process.
| Surface | Best use | What to publish | Main maintenance concern |
|---|---|---|---|
| README and package metadata | Foundational discovery and installation | Purpose, supported environments, install path, minimal example, license and canonical links | Names and examples can drift from released packages |
llms.txt or a documentation index |
Navigation across a documentation set | Short project description and links to getting started, API, security, compatibility, changelog and troubleshooting | Broken, duplicated or stale links reduce its value |
| OpenAPI or an equivalent API catalog | HTTP APIs and workflow discovery | Operations, parameters, schemas, authentication, errors, examples and limits | Generated descriptions can silently diverge from the running API |
| Markdown or specification endpoints | Programmatic retrieval of per-artifact documentation | Readable artifact pages plus machine-readable specifications | Endpoint versioning and access controls must remain predictable |
| Skills or capability indexes | Large collections of supported workflows | Capability names, prerequisites, inputs, outputs and links to instructions | Capability labels must remain aligned with actual behavior |
| MCP server | Direct invocation of stable tools by compatible hosts | Tool schemas, permissions, authentication, side effects, errors, rate limits and examples | Security exposure and contract changes are larger than for static docs |
WSO2 describes a useful pattern: an index leads to per-artifact Markdown and specification endpoints. The same design can be implemented without adopting a particular vendor. The important property is that an agent can discover an operation, read its contract and obtain an invocation example from linked, versioned sources.
Publish an API contract that matches the implementation
If the library includes an HTTP service, publish an accurate OpenAPI document or equivalent description. Include every supported operation, required and optional parameters, request and response schemas, authentication method, pagination behavior, rate limits, status codes and representative examples. Describe error bodies as carefully as successful responses.
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Generate and validate where possible
Generated schemas reduce naming drift, but generation alone is not a guarantee. Run schema validation and an example-based check in continuous integration. Fail the build when a documented parameter disappears, a required field changes, an example no longer parses or an endpoint’s declared response disagrees with the implementation.
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Keep permissions visible
State which credentials are needed for each operation and what those credentials can access. Separate read-only calls from writes, administrative actions and irreversible operations. If an endpoint can trigger billing, deletion, deployment or external communication, put that side effect in the operation summary and example rather than hiding it in a general warning.
Describe limits and retries
Agents need to know whether a request is idempotent, how often it may be called, which failures are transient and whether a retry can duplicate an action. Publish timeout expectations, pagination limits and backoff guidance when those behaviors are part of the service contract. Avoid promising a universal limit if it varies by plan, region or deployment; identify the scope beside the number.
Make trust verifiable instead of rhetorical
Words such as “official,” “secure” and “production-ready” are claims. Evidence lets a user or agent evaluate them.
Show ownership and maintenance
- Name the maintainers or organization responsible for releases.
- Publish a security reporting route and explain how vulnerabilities are handled.
- Show release history, supported versions, end-of-life dates and the deprecation policy.
- Describe dependency review and update practices.
- State whether artifacts are signed or accompanied by provenance attestations, and explain how users can verify them.
The official MCP Registry demonstrates why ownership checks matter: publication requires namespace authentication and evidence that the publisher controls the referenced package. A registry record is therefore a useful signal of control, not a replacement for checking the source, release artifact and permissions yourself.
Make compatibility claims testable
Publish a matrix for language versions, operating systems, architectures, database or service versions and optional integrations. Mark each cell as supported, experimental, unsupported or not tested. Tie the matrix to a release or date so an agent does not apply a current claim to an older package.
Give users a provenance trail
Use a consistent path from repository commit to built artifact and published package. Explain where release files come from, how checksums are obtained and which account or workflow publishes them. If a registry points to an external repository, verify that the repository, domain and package coordinate belong to the expected publisher before installing.
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Decide whether an MCP server is justified
MCP can let compatible hosts call library-backed tools instead of merely reading instructions. That is valuable when operations are stable, permissioned and useful in an agent workflow. It is not an automatic upgrade for every library.
Good candidates for MCP exposure
- The operation has a clear input and output schema.
- Side effects are understood and can be described before invocation.
- Authentication and authorization can be enforced per tool.
- Errors are actionable and rate limits are known.
- Read-only operations can be separated from writes or destructive actions.
What each tool contract must state
- Tool name and purpose in plain language.
- Required and optional inputs, types, allowed values and validation rules.
- Output schema, including empty, partial and paginated results.
- Authentication requirements and data scope.
- Side effects, reversibility and confirmation requirements.
- Expected errors, retry behavior and rate limits.
- A minimal invocation example that uses safe, non-sensitive data.
OpenAI’s MCP guidance warns that prompt injection is especially important when a model can access sensitive data or take action through MCP servers and connectors. Treat every remote server as a third-party service: review its source and operator, limit the data it can reach, require approval for sensitive actions and design the host to resist instructions embedded in untrusted content.
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Keep read-only tools separate from write tools in names, documentation and authorization. A host should be able to allow search while denying deletion. Do not expose a broad administrative function when a narrowly scoped operation can accomplish the task.
Keep agent instructions synchronized with releases
Agent-facing documentation is production documentation. Version it with the code instead of editing it as an unrelated marketing layer.
Put consistency checks in CI
- Build the package and generated API or tool schemas from the release candidate.
- Validate that package metadata, README snippets and documentation examples use the release’s names and version.
- Run every copyable example in a clean environment with the documented runtime.
- Check all index and reference links, including links in
llms.txt. - Compare generated schemas with the implementation and fail on breaking differences that lack a migration note.
- Publish the documentation, artifact and provenance information from the same release workflow.
Handle deprecations explicitly
When an option, endpoint or tool is replaced, document the replacement, the first release containing it, the removal schedule and a before-and-after example. Keep the old name searchable but mark it deprecated. An agent should be able to tell whether it is reading instructions for the installed version or for a newer release.
Separate stable and experimental capabilities
Use visible labels for experimental APIs, preview MCP tools and platform-specific integrations. State that stability, support and compatibility may differ from the main release line. This is more useful than a generic “use at your own risk” sentence because it identifies the exact boundary.
Test the complete agent journey
Review the project from a clean environment, not from the maintainer’s already-configured workstation. The following procedure is a documentation test, not a published performance benchmark.
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- Ask an agent to locate the canonical project from a plain-language description.
- Have it explain why the selected package is the correct one and identify the supported release.
- Start with no cached dependencies or credentials beyond those explicitly documented.
- Install the package using the published command and record every missing prerequisite.
- Run the smallest example exactly as written.
- Perform one common task and inspect the result against the documented output.
- Trigger one documented error, such as an invalid option or missing permission.
- Ask the agent to recover using only the troubleshooting instructions.
- Review every incorrect assumption and fix the responsible name, link, example, schema or warning.
Repeat this review when a major version, package coordinate, authentication flow or MCP tool contract changes. The sources available for this topic provide guidance and protocol requirements, not an authoritative percentage improvement from this exercise, so report defects and fixes rather than inventing a success statistic.
Compare the approaches by the decision that matters
| Approach | Crawlability and indexing | Task completion | Version and compatibility clarity | Trust and security evidence | Operational burden |
|---|---|---|---|---|---|
| README plus package metadata | Basic, if the repository and package page are public and consistent | Strong for a small number of well-written tasks | Must be maintained manually or generated from release data | Can present license, maintainers and provenance links | Lowest; still requires every release to be checked |
README plus llms.txt or indexes |
Better navigation across many documentation pages | Depends on the linked task guides | Clear only when indexes are version-aware | Adds no safety proof by itself | Low to moderate; broken links and stale entries are the main risks |
| API catalog or OpenAPI | Precise discovery of operations and schemas | High for clients that can follow the contract | Strong when generated and versioned with releases | Can document auth and permissions, but does not prove publisher identity | Moderate; validation and example tests are needed |
| MCP server | Discoverable through compatible hosts and registries | Direct invocation, including workflows that change data | Strong only if tool schemas and compatibility are actively managed | Largest security surface; requires ownership checks, approvals and prompt-injection defenses | Highest; server operations, auth, monitoring and incident response become part of the project |
A static README and accurate package metadata are foundational. Add an index when navigation is the bottleneck, an API catalog when clients need formal contracts, and MCP only when direct action provides a clear benefit that justifies its security and operational cost.
Use this implementation sequence
- Inventory the public identity. List every package coordinate, repository, documentation host, registry entry, license identifier and version label. Resolve conflicts before publishing another discovery surface.
- Rewrite the first README screen. Add purpose, audience, non-goals, supported environments, installation, a complete minimal example and links to deeper pages.
- Reorganize by jobs. Create install, configure, common operation, errors, testing, upgrade and troubleshooting paths. Add language and runtime labels.
- Publish the evidence pages. Make license, maintainers, support policy, security contact, compatibility matrix, release history and provenance easy to find.
- Add a small index. Use
llms.txtor an equivalent page to link canonical getting-started, API, security, compatibility, changelog and troubleshooting content. - Describe formal interfaces. Publish OpenAPI or an equivalent catalog for HTTP APIs. Include schemas, examples, authentication, errors, limits and side effects.
- Automate drift detection. Validate links, examples, generated schemas, version labels and package metadata in CI.
- Evaluate MCP deliberately. If stable operations warrant direct invocation, define narrow tools, separate read and write permissions, document every contract detail and require approval for sensitive actions.
- Run the clean-environment journey. Ask an agent to discover, install, execute and recover. Treat each wrong turn as a defect to fix.
Common mistakes and how to correct them
Adding files before fixing identity
A polished index cannot compensate for a repository name that differs from the package coordinate or a documentation site that describes another release. Reconcile names, URLs and versions first.
Publishing a minimal example that is not complete
An example that omits imports, credentials, initialization or expected output forces an agent to invent the missing pieces. Run the example in a clean environment and include every required line.
Using marketing language as a security claim
“Trusted” does not identify who controls the package, how releases are built or where vulnerabilities are reported. Publish ownership, provenance, release and security evidence instead.
Treating a registry listing as an endorsement
AWS Agent Registry documentation states that it stores metadata as publishers provide it and does not verify the packages, repositories or endpoints referenced by records. Validate the publisher, domain, package coordinates, source repository, checksums and requested permissions independently.
Exposing broad tools too early
An MCP server with unrestricted write access creates a larger failure and attack surface than a read-only library guide. Start with narrow, observable operations and add sensitive capabilities only after authentication, approvals, logging and recovery behavior are documented.
Letting generated content drift silently
Generated schemas and indexes can look authoritative while describing an older implementation. Tie generation and validation to the release workflow, and fail builds when examples, parameters or response types change unexpectedly.
A maintenance checklist for every release
- Does the package name match the repository, documentation title and registry coordinate?
- Does the README installation path work on every claimed platform and runtime?
- Does the smallest example run unchanged and show expected output?
- Are task guides, API references and troubleshooting pages linked to one another?
- Does the compatibility matrix identify the release or date it describes?
- Are license, maintainers, security contact, deprecations and provenance current?
- Do
llms.txt, category indexes and Markdown endpoints contain only live, canonical links? - Do API or MCP schemas match implementation names, types, errors, permissions and limits?
- Are write and destructive operations clearly separated from read-only operations?
- Has an agent completed discovery, installation, execution and documented recovery in a clean environment?
The practical standard is simple: an agent should be able to identify the right library, understand its boundaries, install a supported version, complete a normal task and know when not to proceed. Build the evidence and interfaces that make those decisions obvious, then keep them synchronized with every release.
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