Agentic.ai Directory
Server Details
Independent directory of agentic AI tools — search, compare & recommend via MCP. Read-only.
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- Healthy
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- Streamable HTTP
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Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.3/5 across 10 of 10 tools scored.
Most tools have distinct purposes, but the trio of recommend_tools, search_listings, and semantic_search have overlapping search capabilities. Their detailed descriptions help differentiate them, but an agent might still be uncertain which to use first.
All tool names follow a consistent verb_noun pattern (e.g., list_categories, get_listing, compare_listings) using lowercase with underscores. The verbs are descriptive and predictable.
10 tools is well-scoped for a directory server covering browsing, searching, comparing, and detailed information retrieval. Each tool serves a clear purpose without unnecessary redundancy.
The tool set covers the full lifecycle of directory interaction: discovery (categories, recent, tags, search), detailed viewing, comparison, and recommendations. No obvious gaps for a read-only directory.
Available Tools
10 toolscompare_listingsAInspect
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') |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. It mentions the output format but does not explicitly state that the tool is read-only, idempotent, or has no side effects. The description lacks details on authentication, rate limits, or any potential impacts.
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 concise sentences with no waste. It front-loads the purpose, then describes the output, then provides usage guidance and alternatives.
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, full schema coverage, and lack of output schema, the description is mostly complete. It explains what it does and when to use it. However, the output format is described only generically ('structured field matrix and verdict'), leaving some ambiguity. A more detailed output description would improve completeness.
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 covers both required parameters with descriptions. With 100% schema coverage, the description adds no additional meaning beyond what the schema already provides, 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?
The description clearly states the tool compares exactly two AI tools side-by-side and returns a structured field matrix and verdict. It distinguishes itself from sibling tools like search_listings and semantic_search by explicitly stating its use case for decision-making between two specific tools.
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 states when to use this tool ('when a user wants to decide between two specific tools') and provides clear alternatives for finding tools first ('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_agenticness_detailsAInspect
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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It describes the output structure but does not state whether the operation is read-only or if there are authorization or rate limit considerations. It implies a read operation via 'Get', but lacks definitive behavioral disclosure.
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 key information. No superfluous text; 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?
With no output schema, the description explains the output in detail (9 dimensions, scores, rubric version). It also provides usage context via sibling references. Could mention return format but is otherwise thorough.
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 adds context that the slug identifies a tool for agenticness analysis, but does not add significant meaning beyond the parameter's description.
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 returns the full agenticness evaluation breakdown with 9 dimensions scored 0-4 each. It distinguishes itself from siblings by mentioning 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?
Explicitly provides when to use this tool ('deep analysis of one tool's AI agent capabilities') and when to use alternatives ('For a quick score, get_listing... For comparing scores, use compare_listings').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_categoryAInspect
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') |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions sorting by agenticness score but does not state whether the operation is read-only, idempotent, requires authentication, has rate limits, or any side effects. For a simple retrieval, more transparency is needed.
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 clear, front-loaded sentences. The first sentence states purpose and sort order, the second gives usage context, and the third provides alternatives. 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?
For a tool with one parameter, no output schema, and no annotations, the description adequately explains what it does and when to use it. It could mention if pagination exists or response structure, but it's fairly complete for its simplicity.
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% for the single parameter 'slug', which is described with examples. The description adds no new semantic meaning beyond what the schema provides, 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?
The description clearly states the tool's action: 'Get all published listings in one specific category, sorted by agenticness score.' It uses a specific verb and resource, and distinguishes from sibling tools like list_categories, search_listings, and semantic_search.
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 guidance provided: '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.' This tells the agent when to use this tool and when to use alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_listingAInspect
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') |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It explains boolean semantics (null vs false) but does not explicitly state the tool is read-only (implied by 'Get full details'). Good coverage for its simplicity.
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 front-loaded with purpose, followed by usage guidance and a semantic note. No wasted words; succinct and well-structured.
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 one parameter, no output schema, and no annotations, the description covers purpose, usage, and boolean semantics. Missing explicit output structure but adequate for a simple retrieval 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 covers 100% of parameters with description. The description adds context about usage ('by its slug') but does not provide significant additional semantics beyond the schema. Baseline 3 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 clearly states the tool retrieves full details for one specific AI tool by slug, listing included fields. It distinguishes itself from siblings like search_listings (for finding slug) and compare_listings (for comparison).
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 ('when you know the exact tool slug') and provides alternatives for related tasks: use search_listings first to find a slug, and compare_listings for comparing two tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_categoriesAInspect
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 | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses the two cohorts (PEOPLE and TEAMS) and that it returns descriptions and listing counts. This is good context for a simple read operation, though it could mention that it is non-destructive.
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, each providing essential information: action, usage context, and alternative tool. No redundant 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 fully explains what is returned (categories, descriptions, listing counts, cohorts) and how to use it effectively. Also references sibling tool, making it 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?
No parameters exist (schema coverage 100% for empty schema). Baseline is 4 since description needs no compensation. No additional param info needed.
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 'Get all categories with descriptions and listing counts', specifying the verb, resource, and output. It distinguishes from sibling 'get_category' by noting that tool gets listings in a category, while this one gets all categories.
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 discover what categories exist before filtering.' Also provides when-not-to-use guidance: 'To get listings IN a category, use get_category with the slug.' This gives a clear alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_recentAInspect
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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries the burden. It mentions sorting by creation date but does not explicitly state it's read-only, nor describe the response format or any side effects. Adequate for a simple listing tool but leaves some uncertainty.
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: first defines what it does, second states use case, third gives alternatives. Efficiently front-loaded with 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?
For a simple tool with one optional parameter and no output schema, the description explains what is returned (recent listings sorted by creation date) and how to use it. It is nearly complete, though the response format is not specified.
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 a single parameter 'limit' that has a clear description, default, and bounds. The description does not add additional meaning beyond what the schema already provides, so baseline score 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?
The description clearly states the verb ('Get'), resource ('most recently added AI tool listings'), and sorting ('by creation date'). It distinguishes itself from siblings like search_listings and 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 states when to use ('see what's new') and provides alternative tools for specific use cases (search_listings for specific tools, get_category for browsing).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_tagsAInspect
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 | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It clearly states the tool is a read-only tag listing operation and what it does not do. Could mention idempotency or rate limits, but not required 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?
Three concise, front-loaded sentences. Each sentence adds unique value: purpose, usage guidance, and clarification of scope.
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 parameterless tool with no output schema, the description sufficiently explains what is returned (tags grouped by type) and how to use them. Minor omission: exact format of output not described, but still clear.
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 and schema coverage is 100%. The description adds value by explaining the grouping and usage context, which is sufficient for a parameterless tool.
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 ('Get') and resource ('all tags grouped by type') and explicitly distinguishes from siblings by stating 'This does NOT return listings — use search_listings or get_category for that.'
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 discover available filter values.' Also provides negative guidance: 'This does NOT return listings — use search_listings or get_category for that.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recommend_toolsAInspect
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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden of disclosure. It states the tool 'handles search, filtering, scoring, and ranking in one call' and mentions returning 'curated, ranked results with explanations.' It does not explicitly state it is a read-only operation or describe side effects, but it is fairly transparent about its capabilities.
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 paragraphs: overview, usage guidance, and examples. It is front-loaded with the key purpose. The examples are helpful but slightly increase length; overall it is well-structured without unnecessary 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?
Given the absence of an output schema, the description mentions that results are 'curated, ranked results with explanations,' which gives a sense of the return value. It covers input format, constraints, and usage context. Could be more specific about error handling or response structure, but it is adequate 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%, with each parameter having a description. The description adds value through natural language examples and context for the 'question' parameter, which helps the agent understand how to formulate queries. This goes beyond the schema alone.
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 'Get AI-powered tool recommendations for a specific need' and positions it as the recommended starting point. It explicitly distinguishes from siblings like 'search_listings', 'get_listing', and 'compare_listings' by saying 'Use this instead of chaining...'
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 guidance: 'This is the recommended starting point' and tells when to use it instead of alternatives. It gives examples of natural language queries, which further clarifies usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_listingsAInspect
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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. The core behavior is implied by the name and description ('Search for...'), but no additional behavioral traits (e.g., rate limits, authentication, result format, pagination behavior beyond the limit parameter) are disclosed. The description focuses on usage rather than behavioral details.
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 just two sentences, front-loaded with the core purpose, followed immediately by usage alternatives. Every sentence adds value with no redundancy or 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?
Given no output schema, the description could hint at what is returned (e.g., search results), but the tool is straightforward enough that the return type (list of matching tools) is inferable from the name and context. Slight gap in explicit output description, but overall complete for a search tool with 100% parameter coverage and clear sibling differentiation.
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 all 9 parameters described in the input schema. The description does not repeat parameter details nor add any additional semantic context beyond what the schema already provides. Baseline 3 appropriate since the schema does the heavy lifting.
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 searches for agentic AI tools by keyword with optional filters, using a specific verb ('Search') and resource. It distinguishes itself from siblings 'semantic_search' and 'get_category' by specifying when to use each instead.
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 usage guidance is provided: 'Use this for keyword-based search. For natural language queries... use semantic_search instead. For browsing all tools in a category, use get_category instead.' This fully clarifies when and when not to use the tool, with specific alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
semantic_searchAInspect
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 |
Tool Definition Quality
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 mentions 'AI-powered semantic matching' which indicates non-exact matching, but doesn't disclose other behavioral traits such as how results are scored, potential performance characteristics, or what happens with no matches. It could be more transparent about the search behavior.
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 plus a concise example and sibling tool references. It is front-loaded with the main purpose. Slightly more concise could be possible, but it's well-structured 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?
With 9 parameters and no output schema, the description provides high-level context but does not explain return format or pagination behavior (though the limit parameter is described in the schema). It is adequate for a search tool but could be more 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% with all parameters described. The description provides examples of filter usage (e.g., openSource + deploymentModel) but does not add significant semantics beyond what the schema already provides. 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 does 'Search for AI tools using natural language with AI-powered semantic matching'. It distinguishes itself from siblings by specifying that for exact name/keyword searches, one should use search_listings instead, and for comparing specific tools, use compare_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?
The description provides explicit usage guidance: best for conceptual queries like 'something that automates my email workflow' and mentions that structured filters can be applied. It also tells when not to use it (exact name/keyword) and points to alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
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Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
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