The Quiet Protocol Growth Offense MCP
Server Details
Read-only MCP server for The Quiet Protocol's engines, benchmarks, proof, and business data.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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 3.1/5 across 19 of 19 tools scored.
Most tools have distinct purposes, such as 'get_benchmark' for fetching a single benchmark versus 'list_benchmarks' for listing them, or 'run_trust_stack_audit' for scanning trust signals versus 'scan_ai_visibility' for scanning AI readiness. However, 'find_best_resource' and 'select_best_engine' could be confused as both recommend resources, though they target different domains (general resources vs. engines).
Tool names follow a highly consistent verb_noun pattern throughout, such as 'get_resource', 'list_benchmarks', 'run_competitor_intake_scanner', and 'scan_ai_visibility'. All tools use snake_case with clear action prefixes (get, list, run, scan, select, find, pricing), making them predictable and readable.
With 19 tools, the count is borderline high for a business diagnostics and growth server, as it might overwhelm agents with many scanning and benchmarking tools. While the scope is broad (covering resources, benchmarks, audits, and recommendations), a more focused set of 10-15 tools could improve usability without losing functionality.
The tool set provides comprehensive coverage for business growth and diagnostics, including CRUD-like operations (get/list resources, benchmarks, kits), scanning tools for audits and visibility, benchmarking tools for revenue and performance, and recommendation tools for resources and engines. No obvious gaps exist; agents can access all necessary data and analyses for the domain.
Available Tools
20 toolsfind_best_resourceCRead-onlyInspect
Recommend the most relevant public resources or kits for a niche and problem statement.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Optional number of matches to return. | |
| niche | Yes | Business niche or vertical. | |
| problem | Yes | What the operator is trying to fix. |
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 states the tool 'recommends' resources, implying a read-only operation, but doesn't address critical aspects like whether it requires authentication, how it handles rate limits, what the return format looks like, or if it performs any data processing. For a tool with no annotation coverage, this leaves significant gaps in understanding its 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 a single, well-structured sentence that efficiently conveys the core purpose: 'Recommend the most relevant public resources or kits for a niche and problem statement.' It's front-loaded with the main action and includes no redundant information, making it highly concise and easy to parse.
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 lack of annotations and output schema, the description is incomplete for a tool that recommends resources. It doesn't explain what 'recommend' entails (e.g., ranking, filtering, or AI-based selection), the format of returned recommendations, or any prerequisites like authentication. For a 3-parameter tool with no structured behavioral hints, this leaves too much ambiguity for effective agent use.
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 has 100% description coverage, with clear documentation for 'niche,' 'problem,' and 'limit.' The description adds no additional parameter semantics beyond what the schema provides—it mentions 'niche and problem statement' but doesn't elaborate on their meaning or usage. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
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 purpose: 'Recommend the most relevant public resources or kits for a niche and problem statement.' It specifies the verb ('recommend') and resources ('public resources or kits'), and distinguishes its focus on 'most relevant' recommendations. However, it doesn't explicitly differentiate from siblings like 'get_resource' or 'select_best_engine,' which could have overlapping functions.
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 no guidance on when to use this tool versus alternatives. With many sibling tools available (e.g., 'get_resource,' 'select_best_engine,' 'list_resources'), there's no indication of when this recommendation-focused tool is preferred over direct retrieval or other selection methods. The context is implied ('for a niche and problem statement') but lacks explicit usage boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_benchmarkBRead-onlyInspect
Fetch one public benchmark profile by slug.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Benchmark profile slug. |
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 for behavioral disclosure. While 'Fetch' implies a read operation, it doesn't specify whether this requires authentication, has rate limits, returns structured data, or handles errors. The mention of 'public' suggests accessibility but lacks details on permissions or data sensitivity.
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, efficient sentence with zero wasted words. It's front-loaded with the core action and resource, making it immediately scannable and appropriately sized for a simple fetch operation with one parameter.
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 read tool with one parameter and no output schema, the description is minimally adequate. However, without annotations or output details, it lacks information on return format, error handling, or authentication needs. The presence of many sibling tools suggests a complex environment where more contextual guidance would be beneficial.
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 the single parameter 'slug' documented as 'Benchmark profile slug.' The description adds minimal value by restating 'by slug' without providing additional context about slug format, examples, or where to obtain valid slugs. Baseline 3 is 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 action ('Fetch') and resource ('one public benchmark profile by slug'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_benchmarks' or 'get_submission_profile', which would require more specific context about what distinguishes a benchmark profile from other resources.
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 no guidance on when to use this tool versus alternatives. With siblings like 'list_benchmarks' and 'get_submission_profile', there's no indication of when this specific fetch operation is appropriate, what prerequisites exist, or what scenarios warrant its use over other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_kitARead-onlyInspect
Fetch one starter kit by slug, including bundled resource links and download metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Starter kit slug. |
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 discloses the tool fetches data (implied read-only) and includes specific bundled details, but does not mention behavioral traits like error handling, authentication needs, rate limits, or response format. The description adds some context but lacks comprehensive behavioral disclosure for an unannotated 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, efficient sentence that front-loads the core purpose ('Fetch one starter kit by slug') and adds necessary details ('including bundled resource links and download metadata') without redundancy. Every word earns its place, making it highly concise 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 the tool's low complexity (1 parameter, no output schema, no annotations), the description is reasonably complete for a fetch operation. It specifies what is retrieved and the key parameter, though it could benefit from mentioning output format or error cases. Without annotations, it adequately covers basics but has minor gaps in behavioral context.
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 the single parameter 'slug' documented in the schema. The description adds no additional parameter semantics beyond implying 'slug' identifies a starter kit, which is already covered. Baseline 3 is appropriate as the schema handles parameter documentation adequately.
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 specific action ('Fetch') and resource ('one starter kit by slug'), distinguishing it from sibling tools like 'list_kits' (which lists multiple) and 'get_resource' (which fetches a different resource type). It specifies the exact scope of what is retrieved ('including bundled resource links and download metadata').
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 context by specifying 'by slug' and the included data, but does not explicitly state when to use this tool versus alternatives like 'list_kits' or 'get_resource'. It provides clear context for fetching a single kit with full details, though lacks explicit exclusions or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_resourceCRead-onlyInspect
Fetch one free resource by slug, including download metadata when available.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Free resource slug. |
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 'including download metadata when available', which adds some behavioral context about optional return data. However, it lacks details on error handling, authentication needs, rate limits, or what 'fetch' entails operationally, leaving significant gaps for a tool with no annotation coverage.
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, efficient sentence with zero waste. It front-loads the core purpose ('Fetch one free resource by slug') and adds a useful detail ('including download metadata when available') without unnecessary elaboration.
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 annotations and no output schema, the description is incomplete. It doesn't explain what 'fetch' returns beyond 'download metadata when available', leaving the agent uncertain about the response format, error cases, or operational constraints. For a tool with such minimal structured data, more context is needed.
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 the parameter 'slug' fully documented in the schema. The description adds minimal value by implying 'slug' identifies a 'free resource', but doesn't provide additional syntax, format, or examples beyond what the schema already states. Baseline 3 is appropriate when 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 action ('Fetch') and resource ('one free resource by slug'), distinguishing it from list operations like 'list_resources'. However, it doesn't explicitly differentiate from 'find_best_resource' or other get operations like 'get_benchmark' or 'get_kit', which prevents a perfect score.
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 no guidance on when to use this tool versus alternatives like 'find_best_resource' or 'list_resources'. It mentions 'free resource' but doesn't clarify if this is the only tool for free resources or when to use it over other resource-related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_submission_packageBRead-onlyInspect
Return the MCP, directory, and app-submission package with portal requirements and free-account guidance.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 states what is returned but doesn't cover critical aspects like whether this is a read-only operation, if it requires authentication, potential rate limits, or error conditions. For a tool with zero annotation coverage, this is a significant gap in 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?
The description is a single, efficient sentence that front-loads the key action ('Return') and lists the returned components clearly. There's no wasted wording, and it's appropriately sized for the tool's scope. It could be slightly improved by structuring into bullet points for readability, but it's already concise.
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 has no parameters and no output schema, the description provides a basic overview of what is returned. However, it lacks details on the format of the return values (e.g., structured data, links, or documentation), which would help the agent understand how to use the output. With no annotations, it's minimally adequate but has clear gaps.
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 tool has 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description doesn't need to add parameter details, and it doesn't introduce any confusion. A baseline of 4 is appropriate as it compensates adequately by not misleading about 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 the tool returns specific components (MCP, directory, app-submission package) along with portal requirements and free-account guidance. It uses the verb 'Return' with specific resources, making the purpose clear. However, it doesn't explicitly differentiate from sibling tools like 'get_resource' or 'get_submission_profile', which prevents a perfect score.
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 no guidance on when to use this tool versus alternatives. With siblings like 'get_resource' and 'get_submission_profile', there's no indication of what distinguishes this tool's use case, such as whether it's for initial setup, compliance checks, or specific user scenarios. This lack of context leaves the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_submission_profileBRead-onlyInspect
Return the canonical machine-readable business and submission profile for The Quiet Protocol.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool returns a profile but doesn't clarify if this is a read-only operation, whether it requires authentication, or any rate limits. The description is too vague to inform the agent about key behavioral traits beyond the basic action.
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, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It's front-loaded and appropriately sized for a zero-parameter tool, making it highly concise 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 the tool has 0 parameters, no annotations, and no output schema, the description is minimally adequate by stating what it returns. However, it lacks details on the return format, potential errors, or how it differs from siblings, leaving gaps in context for the agent to fully understand its use.
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 tool has 0 parameters, and the schema description coverage is 100%, so there's no need for parameter details in the description. The description appropriately avoids redundant information, earning a high baseline score for not cluttering with unnecessary param semantics.
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 ('Return') and the resource ('canonical machine-readable business and submission profile for The Quiet Protocol'), making the purpose specific and understandable. However, it doesn't explicitly distinguish this tool from its siblings (like 'get_submission_package' or 'get_resource'), which prevents a perfect score.
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 no guidance on when to use this tool versus alternatives, such as the sibling 'get_submission_package'. It lacks context about prerequisites, timing, or exclusions, leaving the agent with minimal direction for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_benchmarksBRead-onlyInspect
List public benchmark profiles by niche, including the related engines and recommended assets.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It implies a read-only operation by using 'List', but doesn't specify if it's safe, requires permissions, or has rate limits. It mentions 'public benchmark profiles', suggesting accessibility, but lacks details on pagination, sorting, or error handling. This is inadequate for a tool with zero annotation coverage.
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, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse. Every part of the sentence contributes to understanding what the tool does, with no wasted 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 complexity is low (0 parameters, no output schema), the description is minimally adequate. It explains what the tool returns but doesn't cover behavioral aspects like safety or performance. Without annotations or an output schema, it should ideally include more about the return format or limitations, but it meets the basic requirement for a simple listing 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?
The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description adds value by clarifying what the tool lists ('public benchmark profiles by niche, including the related engines and recommended assets'), which provides context beyond the empty schema. This compensates well for the simple parameter structure.
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 ('List') and target ('public benchmark profiles'), with additional details about what's included ('related engines and recommended assets'). It distinguishes itself from siblings like 'get_benchmark' by focusing on listing multiple profiles rather than retrieving a single one. However, it doesn't explicitly contrast with 'list_engines' or 'list_kits', which might overlap in scope.
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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as needing authentication or specific contexts, nor does it differentiate from similar tools like 'list_engines' or 'get_benchmark'. This lack of context leaves the agent to infer usage based on the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_enginesBRead-onlyInspect
List flagship public engines exposed by The Quiet Protocol.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 'flagship public engines' but does not specify what data is returned, whether it's paginated, if there are rate limits, or any authentication requirements. This leaves significant gaps in understanding the tool's 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 a single, clear sentence that directly states the tool's purpose without any redundant or verbose language. It is front-loaded and efficiently communicates the essential 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 lack of annotations and output schema, the description is incomplete. It does not explain what 'engines' are in this context, what data is returned, or how to interpret the results, which is insufficient for a tool with no structured behavioral hints.
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 tool has 0 parameters, and the input schema coverage is 100%, so no parameter documentation is needed. The description appropriately does not discuss parameters, earning a baseline score of 4 for not introducing unnecessary information.
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 ('List') and resource ('flagship public engines exposed by The Quiet Protocol'), making the purpose understandable. However, it does not explicitly differentiate this tool from its sibling tools like 'list_benchmarks', 'list_kits', or 'list_resources', which would be needed for a score of 5.
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 no guidance on when to use this tool versus alternatives. It does not mention any specific context, prerequisites, or exclusions, nor does it reference sibling tools like 'select_best_engine' or 'get_resource' for comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_kitsCRead-onlyInspect
List starter kits published by The Quiet Protocol, optionally filtered and limited.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Optional result limit. | |
| query | No | Optional keyword filter across title, audience, and keywords. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states it's a list operation with optional filtering, but doesn't cover critical aspects like whether it's read-only, pagination behavior, error conditions, or rate limits. For a list tool with zero annotation coverage, this is a significant gap in 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?
The description is a single, efficient sentence that front-loads the core purpose ('List starter kits') and briefly mentions optional features. There's no wasted wording, and it's appropriately sized for a simple list 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?
Given no annotations and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., list format, fields included), behavioral traits like pagination, or error handling. For a tool with 2 parameters and no structured support, more context is needed to be fully helpful.
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 schema description coverage is 100%, so the schema already documents both parameters ('limit' and 'query') adequately. The description adds minimal value by mentioning 'optionally filtered and limited', but doesn't provide additional semantics beyond what the schema specifies, such as default values or practical usage examples.
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 ('List') and resource ('starter kits published by The Quiet Protocol'), making the purpose understandable. However, it doesn't explicitly differentiate this tool from sibling tools like 'get_kit' or 'list_resources', which might also retrieve kit-related information, so it doesn't reach the highest score.
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 mentions optional filtering and limiting, but provides no guidance on when to use this tool versus alternatives like 'get_kit' (for a specific kit) or 'list_resources' (which might include kits). There's no explicit context for when to choose this tool over siblings, leaving usage unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_proof_casesCRead-onlyInspect
List representative proof cases and aggregate metrics, optionally filtered by niche.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Optional result limit. | |
| niche | No | Optional niche or keyword filter. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'aggregate metrics' which hints at additional data beyond a simple list, but doesn't specify what metrics, format, or any behavioral traits like pagination, rate limits, or permissions required. This leaves significant gaps for a tool that presumably returns structured data.
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, efficient sentence that front-loads the core purpose and includes the optional filter. There's no wasted text, and it's appropriately sized for the tool's apparent complexity.
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 annotations, no output schema, and a tool that lists data with metrics, the description is incomplete. It doesn't explain what 'representative proof cases' or 'aggregate metrics' entail, nor does it cover behavioral aspects like response format or constraints. For a tool in a server with many siblings, more context is needed.
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 both parameters ('limit' and 'niche'). The description adds marginal value by implying 'niche' is used for filtering, but doesn't provide additional semantics beyond what's in the schema. This meets the baseline for high schema coverage.
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 purpose: 'List representative proof cases and aggregate metrics' with an optional filter. It specifies both the resource (proof cases) and the action (list with metrics), but doesn't distinguish it from potential siblings like 'list_benchmarks' or 'list_resources' that might handle similar listing operations.
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 minimal guidance with 'optionally filtered by niche,' but offers no explicit when-to-use advice, no exclusions, and no alternatives. It doesn't help an agent decide between this tool and similar siblings like 'list_benchmarks' or 'list_resources' for related tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_resourcesBRead-onlyInspect
List free resources published by The Quiet Protocol, optionally filtered by category or limited in count.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Optional result limit. | |
| category | No | Optional resource category slug. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions optional filtering and limiting, but fails to describe critical behaviors such as pagination, sorting, default limits, error handling, or what the output looks like (e.g., list format, fields included). This leaves significant gaps for an agent to understand how to use the tool effectively.
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, well-structured sentence that efficiently conveys the core functionality without any wasted words. It is appropriately sized and front-loaded, making it easy to parse quickly.
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 lack of annotations and output schema, the description is incomplete for a list operation. It doesn't explain what 'resources' entail, how results are returned, or any behavioral nuances like ordering or pagination. For a tool with 2 parameters and no structured output information, this leaves too much unspecified for reliable agent use.
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 input schema already documents both parameters ('limit' and 'category') adequately. The description adds minimal value by mentioning these parameters in a general way ('optionally filtered by category or limited in count'), but doesn't provide additional semantics beyond what the schema offers, such as example categories or limit ranges.
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 purpose with a specific verb ('List') and resource ('free resources published by The Quiet Protocol'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_benchmarks' or 'list_kits' beyond mentioning 'resources' generically.
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 context through 'optionally filtered by category or limited in count,' suggesting when to use these parameters. However, it provides no explicit guidance on when to choose this tool over alternatives like 'find_best_resource' or 'get_resource,' nor does it mention any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
pricing_lookupBRead-onlyInspect
Return The Quiet Protocol offer and pricing summary for agents or buyers that need packaging context.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 states the tool returns a summary, implying a read-only operation, but doesn't disclose behavioral traits such as authentication needs, rate limits, data freshness, or potential side effects. For a tool with zero annotation coverage, this leaves significant gaps in understanding its 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 a single, efficient sentence that directly states the purpose and context without unnecessary words. It is front-loaded with the core action ('Return...') and avoids redundancy, making it highly concise 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 the tool's complexity (simple lookup with no parameters) and lack of annotations/output schema, the description is minimally adequate. It explains what the tool does and for whom, but doesn't cover return format, error handling, or integration details, leaving room for improvement in 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 tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add parameter semantics, but with no parameters, a baseline of 4 is appropriate as there's nothing to compensate for.
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 purpose: 'Return The Quiet Protocol offer and pricing summary' with the target audience 'for agents or buyers that need packaging context.' It uses a specific verb ('Return') and resource ('offer and pricing summary'), though it doesn't explicitly distinguish from siblings like 'get_benchmark' or 'list_benchmarks' which might relate to pricing data.
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 context ('for agents or buyers that need packaging context'), suggesting this tool is for pricing information in packaging scenarios. However, it doesn't provide explicit guidance on when to use this vs. alternatives like 'get_benchmark' or 'list_benchmarks', nor does it mention exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_ai_business_os_diagnosticARead-onlyInspect
Diagnose whether a service business is actually operating like an AI Business Operating System using lead volume, customer value, and the primary systems constraint.
| Name | Required | Description | Default |
|---|---|---|---|
| niche | Yes | Business niche or vertical. | |
| averageValue | Yes | Average booked job, case, or customer value in USD. | |
| monthlyLeads | Yes | Approximate inbound leads per month. | |
| primaryConstraint | Yes | The systems bottleneck that feels most true right now. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, which aligns with a diagnostic tool (no data mutation). The description does not contradict this and implies a read-only analysis. However, it does not disclose whether the tool stores results, requires authentication, or has rate limits. Given that annotations already cover the safety profile, the description adds minimal behavioral context beyond the diagnostic scope.
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 sentence that conveys the core purpose and key inputs. It is front-loaded with the verb 'Diagnose' and the target outcome. While concise, it could benefit from a brief note on the output or a suggestion to use alongside sibling tools for deeper analysis.
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 that the tool has a clear input schema, no output schema, and annotations indicating read-only behavior, the description adequately explains what the tool does and what inputs are needed. It provides enough context for an agent to decide when to invoke this diagnostic compared to more focused tools like run_review_velocity_benchmark or run_response_time_loss_estimator.
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 has 100% coverage with descriptions for all parameters. The description reinforces the purpose of each parameter by grouping them into lead volume, customer value, and systems constraint. This adds context beyond the schema, helping an agent understand the diagnostic framework, though the schema already provides adequate details.
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 purpose: to diagnose whether a service business operates like an AI Business Operating System. It specifies three key inputs (lead volume, customer value, primary constraint) and the diagnostic outcome, which differentiates it from other tools like run_front_door_benchmark or scan_ai_visibility that focus on specific aspects.
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 when to use it: when a user wants a high-level diagnostic of their business operations relative to an AI OS model. However, it does not explicitly mention when not to use it or compare it to siblings like run_trust_stack_audit or run_competitor_intake_scanner, which might be more suitable for specific sub-problems.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_competitor_intake_scannerCRead-onlyInspect
Compare the visible intake posture of your site against a competitor and score where the competitive intake gap is opening.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | Primary city or market. | |
| niche | Yes | Business niche or vertical. | |
| businessUrl | Yes | The business website URL. | |
| competitorUrl | Yes | A competitor website URL. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'compare' and 'score', implying analysis and output generation, but does not detail what the tool actually does (e.g., web scraping, API calls, data processing), potential side effects, rate limits, or authentication needs. This leaves significant gaps in understanding the tool's operation.
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, well-structured sentence that efficiently conveys the core purpose. It is front-loaded with the main action and avoids unnecessary words, though it could be slightly more specific about the output format or methodology to improve clarity without losing conciseness.
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 complexity of a tool that performs competitive analysis with 4 parameters and no output schema or annotations, the description is incomplete. It lacks details on what the output includes (e.g., scores, metrics, recommendations), how the comparison is conducted, and any limitations or dependencies, making it inadequate for full contextual understanding.
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 description does not add meaning beyond the input schema, which has 100% coverage with clear parameter descriptions. It mentions 'niche', 'city', and URLs implicitly through context, but provides no additional details on parameter usage, constraints, or examples. Since schema coverage is high, the 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's purpose: comparing intake posture between your site and a competitor, and scoring the competitive gap. It specifies the verb 'compare and score' and the resources 'your site' and 'competitor', but does not explicitly differentiate from sibling tools like 'run_front_door_benchmark' or 'scan_ai_visibility', which may have overlapping analysis functions.
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 no guidance on when to use this tool versus alternatives. It mentions the context of comparing intake posture, but does not specify prerequisites, exclusions, or recommend other tools for different scenarios, such as using 'run_response_time_loss_estimator' for performance issues or 'scan_ai_visibility' for AI-related audits.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_front_door_benchmarkBRead-onlyInspect
Benchmark the business front door using lead volume, customer value, and current intake profile to estimate monthly and annual revenue at risk.
| Name | Required | Description | Default |
|---|---|---|---|
| niche | Yes | Business niche or vertical. | |
| averageValue | Yes | Average booked job, case, or customer value in USD. | |
| monthlyLeads | Yes | Approximate qualified inbound leads per month. | |
| frontDoorProfile | Yes | Current front-door operating posture. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While it states the tool 'estimates' revenue at risk (implying a calculation/analysis rather than a mutation), it doesn't describe what the output looks like, whether it's a report or single value, if it requires specific permissions, or any rate limits. For an analysis tool with zero annotation coverage, this leaves significant behavioral gaps.
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, well-constructed sentence that efficiently communicates the tool's core function. Every word earns its place: 'benchmark' (action), 'business front door' (target), key inputs, and the output ('estimate monthly and annual revenue at risk'). No wasted words or redundancy.
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 4-parameter analysis tool with no output schema and no annotations, the description is adequate but incomplete. It clearly states what the tool does but lacks information about the output format, any behavioral constraints, or differentiation from similar sibling tools. The 100% schema coverage helps, but the absence of output information and behavioral context keeps this from being fully 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 description coverage is 100%, so the schema already documents all four parameters thoroughly. The description mentions 'lead volume, customer value, and current intake profile' which maps to monthlyLeads, averageValue, and frontDoorProfile parameters, but adds no additional semantic context beyond what's in the schema. The baseline 3 is appropriate when 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's purpose: 'Benchmark the business front door using lead volume, customer value, and current intake profile to estimate monthly and annual revenue at risk.' It specifies the verb ('benchmark'), resource ('business front door'), and key metrics used. However, it doesn't explicitly differentiate from sibling tools like 'run_competitor_intake_scanner' or 'run_response_time_loss_estimator' which might have overlapping domains.
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 no guidance on when to use this tool versus alternatives. With multiple 'run_' sibling tools (competitor_intake_scanner, response_time_loss_estimator, review_velocity_benchmark, trust_stack_audit), there's no indication of when this specific front-door benchmark is appropriate versus those other analysis tools. No prerequisites, exclusions, or comparative context is mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_response_time_loss_estimatorCRead-onlyInspect
Estimate lost bookings and revenue at risk caused by slow first response using lead volume, average value, and average response time.
| Name | Required | Description | Default |
|---|---|---|---|
| niche | Yes | Business niche or vertical. | |
| averageValue | Yes | Average booked job, case, or customer value in USD. | |
| monthlyLeads | Yes | Approximate inbound leads per month. | |
| averageFirstResponseMinutes | Yes | Current average minutes until first human or automated response. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool calculates but doesn't mention whether this is a read-only analysis, what format the output takes, whether it's an estimation vs exact calculation, or any limitations/assumptions. For a calculation tool with zero annotation coverage, this leaves significant behavioral questions unanswered.
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, efficient sentence that communicates the core purpose without unnecessary words. It's appropriately sized for this type of calculation tool and front-loads the key information about what's being estimated and what inputs are required.
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 calculation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the output looks like (lost bookings count? revenue amount? risk percentage?), doesn't mention the calculation methodology or assumptions, and provides minimal behavioral context. Given the complexity of revenue estimation, more completeness is needed.
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 four parameters thoroughly. The description adds minimal value beyond what's in the schema - it mentions the same three inputs (lead volume, average value, average response time) but doesn't provide additional context about how they're used together or any parameter interdependencies.
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 purpose: 'Estimate lost bookings and revenue at risk caused by slow first response' with specific inputs (lead volume, average value, average response time). It distinguishes from siblings by focusing on response time impact rather than benchmarking, scanning, or listing operations. However, it doesn't explicitly contrast with similar tools like run_review_velocity_benchmark.
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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, appropriate contexts, or when other tools like run_front_door_benchmark or run_competitor_intake_scanner might be more suitable. The usage context is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_review_velocity_benchmarkCRead-onlyInspect
Benchmark whether a business is creating enough fresh review proof based on total reviews, recent review pace, and monthly completed jobs or appointments.
| Name | Required | Description | Default |
|---|---|---|---|
| niche | Yes | Business niche or vertical. | |
| totalReviews | Yes | Total public review count today. | |
| reviewsLast90Days | Yes | Reviews added in the last 90 days. | |
| monthlyCompletedJobs | Yes | Approximate completed jobs, visits, or appointments per month. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It describes what the tool does (benchmarking) but doesn't reveal how it behaves: no information about output format, whether it's read-only or mutating, performance characteristics, error conditions, or what 'benchmark' actually means in practice. The description is functional but lacks 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?
The description is a single, well-constructed sentence that efficiently conveys the tool's purpose and key parameters. It's front-loaded with the main action ('benchmark') and wastes no words. Every element serves a purpose in communicating the tool's function.
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 benchmarking tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the benchmark output looks like, what constitutes 'enough' fresh review proof, how the metrics are evaluated, or what actionable insights might result. The agent knows what to input but not what to expect in return or how to interpret results.
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 four parameters thoroughly. The description mentions the same parameters ('total reviews, recent review pace, and monthly completed jobs') but adds no additional semantic context beyond what's in the schema. This meets the baseline expectation when schema coverage is complete.
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 purpose: 'Benchmark whether a business is creating enough fresh review proof' with specific metrics (total reviews, recent review pace, monthly completed jobs). It uses a specific verb ('benchmark') and identifies the resource ('business review proof'), but doesn't explicitly differentiate from sibling tools like 'run_front_door_benchmark' or 'run_competitor_intake_scanner' which might have overlapping domains.
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 no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, conditions for use, or comparisons with sibling tools like 'get_benchmark', 'list_benchmarks', or other 'run_' tools. The agent must infer usage from the purpose alone without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_trust_stack_auditCRead-onlyInspect
Scan a public website and score review signals, proof depth, expert identity, differentiation, and local trust.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | Primary city or market. | |
| niche | Yes | Business niche or vertical. | |
| websiteUrl | Yes | Homepage URL to scan. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions scanning and scoring but lacks details on permissions needed, rate limits, whether the scan is destructive or read-only, or what the output format looks like. This is inadequate for a tool that performs external scanning operations.
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, efficient sentence that front-loads the core action ('scan a public website') and lists the specific scoring dimensions without any wasted words. It is appropriately sized for its 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?
Given the complexity of scanning and scoring a website with no annotations and no output schema, the description is insufficient. It doesn't explain what the scores mean, how they are calculated, or what the tool returns, leaving significant gaps in understanding the tool's behavior and 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?
The schema description coverage is 100%, so the schema already documents all three parameters (websiteUrl, niche, city) adequately. The description adds no additional meaning or context about these parameters beyond what the schema provides, meeting the baseline for high coverage.
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 ('scan a public website') and the specific outputs ('score review signals, proof depth, expert identity, differentiation, and local trust'), making the purpose explicit. However, it doesn't distinguish this tool from its many siblings (like 'run_competitor_intake_scanner' or 'scan_ai_visibility'), which would require a 5.
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 no guidance on when to use this tool versus alternatives like 'run_competitor_intake_scanner' or 'scan_ai_visibility', nor does it mention any prerequisites or exclusions. It simply states what the tool does without contextual usage information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
scan_ai_visibilityBRead-onlyInspect
Scan a public website and score entity clarity, answer coverage, proof, local authority, conversion readiness, and machine readability.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | Primary city or market. | |
| niche | Yes | Business niche or vertical. | |
| websiteUrl | Yes | Homepage URL to scan. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions scanning and scoring but doesn't reveal whether this is a read-only operation, requires authentication, has rate limits, returns structured data, or has side effects. For a scanning tool with zero annotation coverage, this leaves significant behavioral questions unanswered.
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, efficient sentence that front-loads the core action ('Scan a public website') followed by the specific scoring dimensions. Every word contributes essential information with zero redundancy or unnecessary elaboration.
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 complexity (scanning and scoring across six dimensions), lack of annotations, and absence of an output schema, the description is insufficient. It doesn't explain what the scoring output looks like, how scores are calculated, or any behavioral constraints. The agent lacks critical context to properly understand the tool's operation and results.
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 three parameters (websiteUrl, niche, city). The description doesn't add any parameter-specific information beyond what's in the schema. The baseline score of 3 reflects adequate but minimal value addition given the comprehensive schema documentation.
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 specific action ('Scan a public website') and enumerates the six specific scoring dimensions (entity clarity, answer coverage, proof, local authority, conversion readiness, machine readability). It distinguishes this tool from siblings by focusing on website scanning and scoring rather than listing, benchmarking, or other operations.
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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, appropriate contexts, or compare it to sibling tools like 'run_competitor_intake_scanner' or 'run_trust_stack_audit' that might have overlapping functionality. The agent must infer usage from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
select_best_engineBRead-onlyInspect
Recommend the best flagship engine to start with based on business type and the kind of problem being diagnosed.
| Name | Required | Description | Default |
|---|---|---|---|
| goal | Yes | What the user is trying to diagnose first. | |
| niche | Yes | Business niche or vertical. | |
| websiteUrl | No | Optional website URL if a public site exists. |
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 states the tool 'recommends' but doesn't disclose behavioral traits such as whether it's a read-only operation, if it requires authentication, how it handles invalid inputs, or what the output format looks like. The description is minimal and doesn't compensate for the lack of annotations.
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, efficient sentence that front-loads the core purpose without unnecessary details. It's appropriately sized for a recommendation tool, with zero waste or redundancy.
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 annotations, no output schema, and a tool that likely returns a recommendation (a non-trivial output), the description is incomplete. It doesn't explain what the recommendation includes (e.g., engine name, rationale, next steps) or handle edge cases. For a 3-parameter tool with behavioral implications, this is inadequate.
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 parameters ('goal', 'niche', 'websiteUrl') with descriptions and enums for 'goal'. The description adds marginal value by mentioning 'business type' (mapping to 'niche') and 'kind of problem' (mapping to 'goal'), but doesn't provide additional semantics beyond what's in the schema. Baseline 3 is appropriate given high schema coverage.
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 purpose: 'Recommend the best flagship engine to start with based on business type and the kind of problem being diagnosed.' It specifies the verb ('Recommend'), resource ('best flagship engine'), and key inputs ('business type', 'kind of problem'). However, it doesn't explicitly differentiate from sibling tools like 'list_engines' or 'run_competitor_intake_scanner', which might offer similar functionality.
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 context ('based on business type and the kind of problem being diagnosed'), suggesting it's for initial recommendations when users are unsure. However, it lacks explicit guidance on when to use this versus alternatives like 'list_engines' for browsing or specific diagnostic tools (e.g., 'run_competitor_intake_scanner'), and doesn't mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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