Independent directory of agentic AI tools — search, compare & recommend via MCP. Read-only.
Scored across 10 tools
Most tools target distinct operations (browse categories/tags/recent, fetch details, compare, analyze), and descriptions explicitly cross-reference when to use each. However, three retrieval tools—search_listings, semantic_search, and recommend_tools—overlap substantially, with recommend_tools explicitly positioned as a replacement for a search+get+compare chain, which creates a real selection risk.
Names follow a clear verb_noun snake_case pattern (compare_listings, get_listing, list_categories, search_listings, recommend_tools) with consistent style throughout. The lone deviation is semantic_search, which uses an adjective_noun form rather than a verb, but it remains readable and predictable.
Ten tools is well-scoped for a directory server, cleanly covering discovery, detail, comparison, recommendation, and analysis. Each tool earns its place without obvious redundancy or padding.
The read-only surface covers the full discovery lifecycle: keyword and semantic search, category/tag browsing, recent listings, item detail, side-by-side comparison, recommendations, and deep agenticness analysis. Minor gap: no direct 'list listings by tag' operation, though filtered search covers that path.
Compare exactly two AI tools side-by-side. Returns structured field matrix and 'Choose A if... Choose B if...' verdict. Use this when a user wants to decide between two specific tools. For finding tools first, use search_listings or semantic_search.
| Name | Required | Description | Default |
|---|---|---|---|
| slug1 | Yes | First tool's slug (e.g. 'cursor') | |
| slug2 | Yes | Second tool's slug (e.g. 'claude-code') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the return format ('structured field matrix and verdict'), implying read-only behavior. However, it does not explicitly state the tool is read-only or mention any side effects, which is a minor gap.
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 long with no redundant information. It front-loads the core purpose and immediately follows with usage guidance. Every sentence earns its place.
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 (2 parameters, no output schema), the description is complete. It explains the output format and provides usage context. Parameter coverage is full, and behavioral transparency is adequate for a comparison 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 coverage is 100% with both parameters already described clearly in the input schema (string with example slugs). The description does not add extra meaning beyond the schema, so 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 tool compares exactly two AI tools side-by-side, specifying the verb 'compare' and resource 'AI tools'. It distinguishes from sibling tools like search_listings and semantic_search by stating those are for finding tools first.
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 tells when to use this tool ('when a user wants to decide between two specific tools') and when not to ('For finding tools first, use search_listings or semantic_search'). Provides clear alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the full agenticness evaluation breakdown: 9 dimensions (action capability, autonomy, planning, adaptation, state continuity, reliability, interoperability, safety, operator sovereignty) scored 0-4 each (max 36, Agenticness rubric v3.1) with evidence-based reasoning. Use this for deep analysis of one tool's AI agent capabilities. For a quick score, get_listing includes the overall score. For comparing scores, use compare_listings.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The listing slug |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry full burden. It discloses the output structure (9 dimensions, scores, evidence-based reasoning) and mentions the scoring rubric v3.1. It does not mention side effects or authentication, but as a read-only retrieval, the description is adequate. A slight deduction for not explicitly stating it is non-destructive or read-only.
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 extremely concise at two sentences, front-loaded with the primary purpose, and contains no fluff. 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?
The description covers the output details (9 dimensions, scoring, evidence) despite no output schema. It mentions the rubric version. It does not explain the format of the output (e.g., JSON structure), but it's sufficient for an agent to understand what it will receive. Slight deduction for missing format details.
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% with one parameter 'slug' described. The description does not add extra meaning beyond the schema; it merely echoes that the tool is for a single tool. With high schema coverage, baseline is 3, and no additional value is provided.
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 (Get) and the resource (full agenticness evaluation breakdown) with specific details: 9 dimensions, scoring 0-4 each, plus evidence-based reasoning. It also explicitly distinguishes this tool from siblings by stating that get_listing provides a quick score and compare_listings is for comparisons.
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 provides explicit when-to-use (deep analysis of one tool's AI agent capabilities) and when-not-to-use (for quick score use get_listing, for comparisons use compare_listings) guidance, making it very clear for an agent to decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get all published listings in one specific category, sorted by agenticness score. Use this to browse a category. To see all categories first, use list_categories. To search across ALL categories, use search_listings or semantic_search.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The category slug (e.g. 'coding-agents', 'general-purpose-agents') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries full burden. It discloses sorting by agenticness score and that only published listings are returned. However, it does not mention pagination, authentication, or rate limits, which would improve transparency for a read 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?
Three sentences with no redundant text. The main purpose is front-loaded, followed by usage guidance. 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?
Given the single required parameter, no output schema, and simple task, the description covers browsing a category and sorting. Missing details like pagination or response format, but still 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?
Schema coverage is 100%, so baseline is 3. The description implies the single 'slug' parameter but does not add new semantics beyond the schema's example values and explanation.
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 states 'Get all published listings in one specific category, sorted by agenticness score.' It clearly identifies the verb, resource, and scope, and differentiates from siblings like list_categories and search_listings.
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?
Explicitly states when to use: 'Use this to browse a category.' Also provides alternatives: 'To see all categories first, use list_categories. To search across ALL categories, use search_listings or semantic_search.'
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 one specific AI tool by its slug — includes features, pricing, agenticness scores, and structured attributes. Use this when you know the exact tool slug. To find a slug, use search_listings first. For comparing two tools, use compare_listings. Note: null on boolean fields means 'unknown', false means 'confirmed no'.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The listing slug (e.g. 'cursor', 'claude-code', 'openclaw') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description adds crucial behavioral context beyond schema: explains that null on boolean fields means 'unknown' while false means 'confirmed no'. This is valuable for interpreting results. Lacks mention of any side effects, but as a read-only lookup this is acceptable.
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 front-loaded with purpose, then usage guidance, then a note on boolean semantics. No fluff.
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 retrieval tool with one required parameter and no output schema, the description sufficiently explains what it returns and provides a key nuance about null/false. Could be slightly more explicit about the output format (single object) but overall 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 description coverage is 100% with a clear description of the slug parameter. The tool description does not add additional semantic detail beyond what the schema already provides, so baseline 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 tool retrieves full details for one specific AI tool by slug, explicitly mentioning included fields (features, pricing, agenticness scores, structured attributes). It differentiates from siblings like compare_listings and search_listings.
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?
Provides explicit when-to-use (when slug is known), when-not-to (need slug first), and alternatives (search_listings to find slug, compare_listings for comparison). Perfect guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get all categories with descriptions and listing counts. Use this to discover what categories exist before filtering. To get listings IN a category, use get_category with the slug. Categories are split into PEOPLE (individual use) and TEAMS (team/enterprise) cohorts.
| 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 full burden. It discloses the returned data (descriptions, listing counts) and the category split. However, it does not mention potential pagination, ordering, or any rate limits. For a simple list tool, this is mostly sufficient but lacks a small detail on response structure.
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 concise with three sentences. The main action is front-loaded, and every sentence adds value: first sentence states what it does, second provides usage guidance, third explains additional structure.
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 (no parameters, no output schema), the description is complete. It includes enough context for an agent to understand what the tool returns and how it fits with siblings. No additional information is necessary.
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?
No parameters exist, so per guidelines the baseline is 4. The schema coverage is 100% trivially. The description adds no parameter information, but none is needed since there are no parameters.
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 gets all categories with descriptions and listing counts. It uses the specific verb 'get' and resource 'all categories', making the purpose unambiguous. It also distinguishes from the sibling tool get_category by noting that get_category is for listings within a category.
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 provides when to use this tool ('discover what categories exist before filtering') and when not to ('to get listings IN a category, use get_category'). It also explains the two cohorts (PEOPLE and TEAMS), giving practical context for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the most recently added AI tool listings, sorted by creation date. Use this to see what's new. For finding specific tools, use search_listings. For browsing by category, use get_category.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of listings to return |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses sorting by creation date and that it shows most recently added, but does not explicitly state it's read-only or whether auth is needed. Adequate but not rich.
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 sentences with no wasted words. First sentence states purpose, second indicates use case, third gives clear alternatives. Information is front-loaded and efficient.
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 1 optional parameter and no output schema, the description adequately covers what it returns (listings, sorted by creation date) and the limit parameter. Could mention listing fields but not essential.
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% for the single parameter 'limit' with a clear description. The tool description adds no extra semantic meaning beyond what the schema provides, so baseline 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?
Description clearly states it retrieves recently added AI tool listings sorted by creation date. It also distinguishes from siblings by mentioning search_listings and get_category as alternatives.
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?
Explicitly says when to use this tool vs alternatives: for finding specific tools use search_listings, for browsing by category use get_category. Lacks explicit when-not-use but provides clear context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get all tags grouped by type (pricing, platform, capability, deployment, model, autonomy, use-case). Use this to discover available filter values. Tags can be used as filters in search_listings. This does NOT return listings — use search_listings or get_category for that.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It implies a read-only operation but does not explicitly confirm safety, auth requirements, or rate limits. The grouping structure is disclosed but behavioral traits are not fully addressed.
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 sentences with no waste. First sentence defines purpose, second gives usage context, third clarifies limitations. Front-loaded and efficient.
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?
Even without an output schema, the description sufficiently explains the grouped output and connects to sibling tools. Complete for a simple list tool with no parameters.
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?
No parameters exist, baseline 4. Description adds value by explaining the output grouping and purpose, which goes beyond the empty schema.
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 it retrieves all tags grouped by type, listing specific categories. It distinguishes itself from siblings by explicitly stating it does not return listings, redirecting to search_listings or get_category.
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?
Explicitly says to use this to discover available filter values and that tags can be used as filters in search_listings. Also states what it does not do, providing clear when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get AI-powered tool recommendations for a specific need. This is the recommended starting point — describe what you're looking for in natural language and get curated, ranked results with explanations. Handles search, filtering, scoring, and ranking in one call. Use this instead of chaining search_listings + get_listing + compare_listings.
Examples:
"best coding agent for a small startup on a budget"
"open source alternative to Cursor for VS Code"
"autonomous customer support agent with MCP support"
"self-hosted data analysis tool for enterprise"
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes | Natural language description of what you need. Be specific about your use case, team size, budget, deployment preferences, etc. | |
| constraints | No | Optional structured constraints to narrow results |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, and it does disclose meaningful behavior: it handles 'search, filtering, scoring, and ranking in one call' and returns 'curated, ranked results with explanations.' That tells the agent the composite work done internally and the shape of the return. It stops short of stating read-only safety or any latency/determinism characteristics.
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?
Front-loaded with purpose, then routing guidance, then examples. All sentences earn their place, though four examples is on the heavier side; the tradeoff is justified for a natural-language entry-point tool.
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 compensates by stating results are 'curated, ranked' and include 'explanations,' so the agent knows what comes back. Combined with clear routing guidance and a fully documented schema, it is complete enough for correct invocation.
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, but the examples add value beyond the schema by showing how free-text questions map to implicit constraints (open source, self-hosted, MCP support, budget, team size). It still doesn't explicitly direct the agent to the structured 'constraints' object, keeping it just above baseline.
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?
States a specific verb+resource ('Get AI-powered tool recommendations') and immediately scopes it to a specific need. It explicitly names the siblings it replaces (search_listings + get_listing + compare_listings), so an agent can distinguish it from the other nine tools without opening a schema.
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?
Explicitly declares itself 'the recommended starting point' and gives a concrete when-not: 'Use this instead of chaining search_listings + get_listing + compare_listings.' The natural-language examples further anchor when to reach for it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for agentic AI tools by keyword query with optional filters. Use this for keyword-based search. For natural language queries like 'something that automates email', use semantic_search instead. For browsing all tools in a category, use get_category instead.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results to return | |
| query | Yes | Search query (e.g. 'code review', 'open source coding agent') | |
| cohort | No | Filter by cohort: PEOPLE (individual tools) or TEAMS (team/enterprise tools) | |
| category | No | Filter by category slug (e.g. 'coding-agents', 'general-purpose-agents') | |
| minScore | No | Minimum agenticness score (default 1 to exclude unscored/junk entries, set to 0 to include all) | |
| mcpSupport | No | Filter to tools with MCP (Model Context Protocol) support | |
| openSource | No | Filter to open-source tools only | |
| autonomyLevel | No | Filter by autonomy level | |
| deploymentModel | No | Filter by deployment model |
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 implies a safe read operation but does not disclose return format, pagination behavior, result ordering, or whether there are rate limits. Useful routing context, but little operational transparency.
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 short sentences, front-loaded with the core action, and each sentence earns its place by either stating scope or routing to an alternative. No filler.
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 read-only search tool with nine fully documented parameters and a required query, the description covers selection and routing adequately. The only gap is return shape and pagination, which matter less here since there is no output schema and limit/default are already in the schema.
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 defaults, min/max, and enums all documented in the schema. The description mentions keyword query and optional filters but adds no format, syntax, or interaction detail beyond that, so the baseline of 3 applies.
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?
States a specific verb+resource ('Search for agentic AI tools') plus the query mode and the presence of optional filters. It explicitly differentiates itself from siblings semantic_search and get_category, so an agent can pick the right tool without opening schemas.
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?
Gives explicit when-to-use guidance ('keyword-based search') and names two alternatives with the conditions that select each ('natural language queries ... use semantic_search', 'browsing all tools in a category ... use get_category'). This is textbook routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for AI tools using natural language with AI-powered semantic matching. Best for conceptual queries like 'something that automates my email workflow'. Supports structured filters to narrow results (e.g., openSource + deploymentModel). For exact name/keyword searches, use search_listings instead. For comparing specific tools, use compare_listings.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results to return | |
| query | Yes | Natural language search query | |
| cohort | No | Filter by cohort | |
| category | No | Filter by category slug | |
| minScore | No | Minimum agenticness score (0-36) | |
| mcpSupport | No | Filter by MCP (Model Context Protocol) support | |
| openSource | No | Filter by open source status (true/false) | |
| autonomyLevel | No | Filter by autonomy level | |
| deploymentModel | No | Filter by deployment model |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It usefully discloses that matching is semantic rather than literal and that structured filters can be combined to narrow results, but says nothing about result ordering/relevance scores, how filters interact with the semantic ranking, or any limits or failure modes.
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 tight sentences, all front-loaded: what it does, then the example, then the two alternative routes. Every sentence earns its place with no redundancy or filler.
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 9-parameter tool with no output schema and no annotations, the description covers purpose and routing well but omits the shape of the results (ranked list? scores?) and how filtering interacts with semantic ranking. Adequate but with a real gap for an agent deciding how to interpret output.
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 all 9 parameters (including 3 enums) documented inline, so the baseline is 3. The description adds only a light example of combining filters (openSource + deploymentModel) and no syntax or semantics beyond the schema.
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?
States a specific verb (search) and resource (AI tools) with the distinguishing mechanism (natural language semantic matching). It explicitly separates itself from siblings by naming 'search_listings' for keyword queries and 'compare_listings' for comparisons, so the agent can select correctly without opening a schema.
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?
Provides an explicit when-to-use case ('conceptual queries like "something that automates my email workflow"'), a concrete example, and two named alternatives with the conditions that select them. Nothing about routing is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
recommend_tools1 field changedInput schema / properties / question / maxLengthAdded value: +500search_listings1 field changedInput schema / properties / query / maxLengthAdded value: +500semantic_search1 field changedInput schema / properties / query / maxLengthAdded value: +500compare_listingsget_agenticness_detailsget_categoryget_listinglist_categorieslist_recentlist_tagsrecommend_toolssearch_listingssemantic_searchAI tool directory MCP server, live since July 2026. Tools, agents, models, companies, services.
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