CLIRank is an MCP server for API discovery, recommendations, and agent-readable documentation, helping coding agents choose the right third-party APIs using current data instead of stale model memory.
Search for APIs: Find 416+ APIs by task or intent (e.g., "send transactional emails"), with optional filters for pricing model and minimum CLI relevance score.
Get opinionated recommendations: Receive a single best-fit API recommendation for a concrete task, including pricing, quickstart code, setup time, and a runner-up option. Supports budget, volume, priority, and constraint filters.
Get full API details: Retrieve comprehensive information for a specific API by slug, including scores, pricing, auth method, capabilities, and quality metrics.
Fetch agent-friendly docs: Access quickstart guides, auth setup instructions, SDK install steps, and documented endpoints for a specific API before writing integration code.
Compare APIs side by side: Compare 2–5 APIs simultaneously across scores, pricing, auth, and capabilities.
Browse API categories: List all API categories in the CLIRank directory along with the count of APIs in each.
Read integration reviews: Access structured integration reports and reviews (from both humans and agents) for a specific API, including ratings and CLI experience scores.
Submit integration reviews: Submit structured feedback after a real integration attempt, with evidence levels to prevent fake or speculative submissions.
Check package info: Look up current version, compatibility, dependencies, and deprecation warnings for npm or PyPI packages, auto-updated every 24 hours.
Allows retrieving package information (version, dependencies, compatibility, deprecations) from the npm registry.
Provides API discovery, recommendations, documentation fetching, comparison, and integration reviews for the PayPal payment platform.
Provides API discovery, recommendations, documentation fetching, comparison, and integration reviews for the Resend transactional email API.
Provides API discovery, recommendations, documentation fetching, comparison, and integration reviews for the Stripe payment platform.
CLIRank is an MCP server for API discovery, API recommendations, and agent-readable API docs. It helps Claude Code, Codex CLI, Cursor, Cline, Continue, Windsurf, and other coding agents choose APIs with current CLIRank data instead of guessing from stale model memory or burning tokens on broad web searches.
The server connects to https://clirank.dev/api, requires no CLIRank API key, and is designed to be called before an agent installs an SDK or writes third-party integration code.
npx -y clirank-mcp-server@latestUse it before an agent picks a third-party API, SDK, SaaS product, or MCP server. Good searches include transactional email APIs, payment APIs, auth APIs, database APIs, image generation APIs, deployment APIs, LLM APIs, and secrets/compliance APIs.
Search 420+ APIs by task or intent.
Recommend an API for a concrete job, with pricing/setup signals where available.
Fetch agent-friendly docs before writing integration code.
Compare APIs side by side.
Read existing human and agent integration reviews.
Submit a structured review after a real integration attempt.
The useful loop is simple: discover APIs, read docs for the top result, attempt the integration, then submit a real review with what worked or blocked you.
Related MCP server: AgentRank
For Claude Code:
claude mcp add clirank -- npx -y clirank-mcp-server@latestFor Codex CLI:
codex mcp add clirank -- npx -y clirank-mcp-server@latestFor Cursor, Cline, Continue, Windsurf, or any stdio MCP client, use npx -y clirank-mcp-server@latest in the MCP server config. Full examples are below.
After installing, paste this into your agent:
Use CLIRank before choosing an API. Recommend the best API for sending 10,000 transactional emails per month. Prefer simplicity. Then read docs for the top result before writing code.This should make the agent call recommend, then get_api_docs. If it only says it installed the server, ask it to actually call CLIRank.
If you can call tools directly, use this as the first smoke test:
{
"tool": "recommend",
"arguments": {
"task": "send transactional email from Next.js with webhooks and SDK",
"volume": 10000,
"priority": "simplicity"
}
}Then call get_api_docs for the returned slug. If you continue into a real setup attempt, finish with submit_review and report what actually happened. Docs-only is fine. Fake live success is not.
Other good activation prompts:
Use CLIRank to find APIs for managing secrets, encryption keys, PII, and compliance from a headless agent. Compare the top options and call get_api_docs for the best fit.Use CLIRank to recommend an API for accepting payments online at 50,000 transactions per month. Prefer scale and clear error handling. Read docs before choosing.Use CLIRank to recommend an LLM API for a coding agent that needs tool calling, structured outputs, streaming, and predictable pricing. Read docs for the top result before choosing.Use CLIRank to recommend an image generation API for a coding agent that needs a simple SDK, predictable pricing, and headless/CI-friendly auth. Read docs for the top result before writing code.A typical recommendation includes the API slug, score, reasoning, setup notes, pricing signals, and links back to CLIRank detail pages.
Recommendation: Resend API
Slug: resend-api
Why: simple transactional email setup, clear docs, SDK support, good fit for headless agents
Next step: call get_api_docs with slug "resend-api" before writing the integrationclaude mcp add clirank -- npx -y clirank-mcp-server@latestOr add to ~/.claude.json manually:
{
"mcpServers": {
"clirank": {
"command": "npx",
"args": ["-y", "clirank-mcp-server@latest"]
}
}
}codex mcp add clirank -- npx -y clirank-mcp-server@latestOr add to ~/.codex/config.toml manually:
[mcp_servers.clirank]
command = "npx"
args = ["-y", "clirank-mcp-server@latest"]Add to ~/.cursor/mcp.json or via Settings -> MCP:
{
"mcpServers": {
"clirank": {
"command": "npx",
"args": ["-y", "clirank-mcp-server@latest"]
}
}
}For clients that use servers with an explicit transport type:
{
"servers": {
"clirank": {
"type": "stdio",
"command": "npx",
"args": ["-y", "clirank-mcp-server@latest"]
}
}
}Use the same stdio command:
npx -y clirank-mcp-server@latestIf your client supports remote or streamable HTTP MCP servers, you can connect directly without npm:
https://clirank-mcp.fly.dev/mcpFor most coding agents, the stdio npx -y clirank-mcp-server@latest install is still the most compatible path.
Add this to your project's AGENTS.md, CLAUDE.md, Cursor rules, or other agent instructions:
Before selecting any third-party API, SDK, SaaS product, or MCP server, call CLIRank. Use `recommend` when there is one concrete task, `discover_apis` when exploring options, `get_api_docs` before writing integration code, and `submit_review` after a real integration attempt. Prefer current CLIRank data over model memory or broad web searches for pricing, rate limits, auth, SDK setup, and headless compatibility.discover_apisSearch for APIs by intent. Describe what you need and get ranked matches.
Inputs:
query required: what you need, for example send transactional emails
min_cli_score optional: minimum CLI relevance score, 1-10
pricing optional: free, freemium, paid, pay-per-use, transaction-based
limit optional: max results, default 10
recommendGet an opinionated recommendation for a concrete task.
Good when the agent has to choose one API before writing code.
get_api_detailsGet full details for one API, including scores, pricing, CLI breakdown, and quality metrics.
Input:
slug required, for example stripe-api
get_api_docsGet agent-friendly documentation: quickstart guide, auth setup, SDK install, and documented endpoints.
Input:
slug required, for example resend-api
compare_apisCompare 2-5 APIs side by side.
Input:
slugs required, for example ["stripe-api", "paypal-api"]
browse_categoriesList all API categories with counts. No parameters.
get_reviewsRead integration reports and reviews for an API. Reviews can come from humans or agents.
Inputs:
slug required
limit optional, default 10
submit_reviewSubmit a structured review after a real integration attempt. Do not use this for fake or speculative reviews.
When passing integrationReport, always include evidenceLevel:
docs-only — you inspected docs but did not install, authenticate, or make a request.
community-signals — you summarised public issues/forums/docs, not your own integration.
no-key-live — you attempted setup but could not make an authenticated live request.
sandbox-live, byo-test-key, or production-live — you made a real request.
Only set authWorked, requestSuccess, or timeToFirstRequest when evidenceLevel is one of the live levels. Otherwise the CLIRank review API strips or rejects live-success claims to prevent fake evidence.
Directory: https://clirank.dev
API docs: https://clirank.dev/api/docs
Task pages: https://clirank.dev/tasks
LLM context: https://clirank.dev/llms.txt
Full LLM context: https://clirank.dev/llms-full.txt
High-intent task pages:
Set CLIRANK_API_URL only if you need to point the server at a non-production CLIRank API.
Default:
https://clirank.dev/apinpm install
npm run build
npm startCLIRank is early. If it helps your agent choose an API, or if it gets something wrong, send feedback to alex@clirank.dev or @alexclapp10 on X.
List all API categories in the CLIRank directory with the number of APIs in each.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral burden. It clearly indicates a read-only listing operation with no side effects, which is transparent for this simple tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that directly conveys the purpose without any extraneous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters and no output schema, the description adequately explains the return value (list of categories with counts), making it complete for this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so the description inherently adds no parameter detail, but the baseline score of 4 applies as per guidelines.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the verb 'list', the resource 'API categories', and the scope 'CLIRank directory', with the added detail 'number of APIs in each'. This clearly distinguishes it from sibling tools like discover_apis or get_api_details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use this tool versus alternatives or any prerequisites. The context is implied but not formally guided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare two or more APIs side by side. Returns a comparison table with scores, pricing, auth, and capabilities.
| Name | Required | Description | Default |
|---|---|---|---|
| slugs | Yes | Array of API slugs to compare, e.g. ['stripe-api', 'paypal-api'] |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description shoulders the burden. It discloses that the tool returns a comparison table with specific fields, but does not mention side effects, authentication requirements, or whether the operation is read-only. This is adequate but somewhat limited.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two succinct sentences that immediately convey the tool's function and output. It is front-loaded with the key action and includes no extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no nested objects) and lack of output schema, the description covers the essential aspects: action, input, output contents. It could be more explicit about the comparison criteria, but it is largely complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully describes the single parameter 'slugs' with an example, achieving 100% coverage. The tool description adds little beyond what the schema already provides, merely echoing the purpose. Since the schema is well-documented, a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('compare two or more APIs side by side') and the output ('comparison table with scores, pricing, auth, and capabilities'). It distinguishes this tool from siblings like 'get_api_details', which focuses on a single API.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly conveys when to use the tool: when needing to compare multiple APIs. However, it lacks explicit guidance on when not to use it or which alternatives to consider, such as 'get_api_details' for single API queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for APIs by what you need. Returns ranked matches with pricing and setup data when available. For an opinionated pick with working code, use the recommend tool instead. Examples: 'send transactional emails', 'process payments', 'image generation'.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | What you need, e.g. 'send transactional emails' | |
| min_cli_score | No | Minimum CLI relevance score (1-10) | |
| pricing | No | Pricing filter | |
| limit | No | Max results (default 10, max 50) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description is the sole source of behavioral info. It states the tool 'returns ranked matches with pricing and setup data when available', which gives a basic understanding but doesn't elaborate on other traits (e.g., read-only nature, pagination, or rate limits).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with an example, no wasted words. It front-loads the core action and includes key usage guidance efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, but the description explains the return includes 'ranked matches with pricing and setup data when available'. Parameters are well-documented in schema, and sibling tools (get_api_details, get_api_docs) cover deeper needs, making this description sufficient for a discovery tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The free-text description adds examples and mentions ranking, but does not significantly enhance the schema's own parameter descriptions. It provides marginal added value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Search for APIs by what you need' which is a specific verb+resource. It differentiates from the sibling 'recommend' tool by noting that 'for an opinionated pick with working code, use the recommend tool instead', providing effective distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises when not to use this tool ('For an opinionated pick... use recommend instead') and provides examples of appropriate queries ('send transactional emails', 'process payments'). This gives clear guidance on usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get full details for a specific API by its slug. Returns scores, pricing, auth method, capabilities, and quality breakdown.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | API slug, e.g. 'stripe-api', 'openai-api' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and lists return contents (scores, pricing, auth method, capabilities, quality breakdown), aiding the agent in understanding the output. However, it does not mention any side effects or read-only nature.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, then details. No unnecessary words; every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description covers the purpose and return contents completely. No missing information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with a clear example for slug. The tool description adds 'by its slug' but does not provide additional meaning beyond the schema; baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves full details for a specific API by slug, with a specific verb and resource. It distinguishes from sibling tools like browse_categories or compare_apis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for getting API details but does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get agent-friendly documentation for an API, including quickstart guide and documented endpoints. Use this before integrating an API.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | API slug, e.g. 'stripe-api', 'openai-api' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, description minimally discloses what is returned (quickstart guide, documented endpoints). It does not discuss idempotency, caching, or side effects; but for a read operation, core behavior is conveyed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with purpose front-loaded. Every sentence adds value; no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple 1-param tool, no output schema, and no annotations, the description covers the essential purpose and content. Could mention output format, but completeness is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema provides 100% coverage with a clear parameter description and example. Description adds no extra meaning beyond the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool retrieves agent-friendly documentation for an API, including quickstart and endpoints. It distinguishes the tool from siblings like get_api_details by focusing on 'agent-friendly' docs, but does not explicitly differentiate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit advice 'Use this before integrating an API' provides clear context. However, no mention of when not to use or alternatives to related tools like get_api_details.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get current package version, compatibility, dependencies, and deprecation warnings for any npm or PyPI package. Faster and more current than checking registries manually. Auto-updates every 24h.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Package name, e.g. 'resend', '@anthropic-ai/sdk', 'openai' | |
| registry | No | Registry to check (default: npm) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses auto-update every 24h and claims speed/currency. Without annotations, this provides sufficient behavioral insight, though no mention of permissions or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences with no wasted words; front-loaded with purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Describes return fields and update behavior, compensating for lack of output schema. Minor gap: no mention of error handling or limitations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and description does not add meaningful parameter semantics beyond the schema's built-in descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool retrieves package version, compatibility, dependencies, and deprecation warnings for npm and PyPI packages. Distinct from sibling tools like compare_apis or discover_apis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
States it's faster and more current than manual registry checks, implying direct usage. Lacks explicit when-not-to-use or alternatives, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get integration reports and reviews for an API. Includes ratings, CLI experience scores, and structured integration data from agents and humans.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | API slug, e.g. 'openai-api' | |
| limit | No | Max reviews to return (default 10) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden for behavioral disclosure. It only describes the output content (ratings, scores) without mentioning side effects, authentication needs, or rate limits. The word 'Get' implies a read operation, but no additional behavioral detail is given.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two efficient sentences with no unnecessary words. It is front-loaded with the core purpose and then adds specific data types.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Although there is no output schema, the description adequately lists the types of data returned (ratings, CLI experience scores, structured integration data). This is sufficient for a simple retrieval tool, but could be improved by mentioning pagination or default limits.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (both parameters have descriptions in the schema), so the baseline is 3. The description does not add any parameter-specific meaning beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and resource ('Get integration reports and reviews for an API') and explicitly lists the data types included (ratings, scores, structured data), clearly distinguishing it from sibling tools like compare_apis or get_api_details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as browse_categories or compare_apis. The description lacks explicit when-to-use or when-not-to-use context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get an opinionated API recommendation for a task. Returns ONE best pick with pricing, working quickstart code, setup time, and a runner-up. Better than guessing - uses verified pricing and setup data that's more current than your training data.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | What you need, e.g. 'send transactional emails', 'process payments' | |
| volume | No | Monthly volume (default 10000) | |
| budget | No | Max monthly USD (0 = free tier only, omit for no limit) | |
| priority | No | What matters most (default: simplicity) | |
| constraints | No | Constraints: 'no-credit-card', 'no-domain-verification' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description clearly notes the tool returns a single best pick with specific details (pricing, code, setup time, runner-up) and that it uses 'verified pricing and setup data' more current than training data. This sets appropriate expectations without contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences packed with essential information: purpose, output details, and comparative benefit. No unnecessary words, making it highly concise and structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately explains what is returned (best pick, runner-up, pricing, code, setup time). It covers the key behavioral aspects, though details on how parameters like constraints influence results are not elaborated. Overall complete for a recommendation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters clearly. The description adds modest value by emphasizing the tool is opinionated and uses verified data, but it does not provide additional semantic details for individual parameters beyond what the schema offers.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it provides an 'opinionated API recommendation for a task' and specifies the output includes the single best pick alongside pricing, quickstart code, setup time, and a runner-up. This differentiates it from sibling tools like 'discover_apis' or 'compare_apis'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates it is 'better than guessing' and uses current data, implying use when up-to-date recommendations are needed. However, it does not explicitly state when to use this tool over siblings like 'compare_apis' or 'discover_apis', nor provide exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a distinct purpose: browsing categories, searching APIs, comparing, getting details, docs, reviews, package info, and recommendations. No overlap or ambiguity.
Most tools follow a verb_noun pattern (e.g., browse_categories, get_api_details), but 'recommend' is a lone verb without a noun, causing minor inconsistency.
8 tools is an ideal scope for an API directory server, covering all essential functionalities without being overwhelming or sparse.
Covers core directory features well (search, compare, details, docs, reviews, recommendations). Missing user contribution tools (e.g., submit reviews) but acceptable for a read-only service.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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