ground-truth-mcp

Server Quality Checklist

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Latest release: v0.5.1
Disambiguation5/5
Each tool has a clearly defined purpose with minimal overlap. While some tools like verify_claim and test_hypothesis might seem similar, their descriptions distinctly separate them: verify_claim does keyword matching on given URLs, while test_hypothesis runs a small plan of varied checks. Other tools like check_pricing and compare_pricing_pages are also well-differentiated by scope.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., assess_compliance_posture, check_endpoint, list_monitors). There are no deviations or mixed conventions, making it easy for an agent to predict tool names.
Tool Count5/5
With 16 tools, the server covers a wide range of verification scenarios without being bloated. The count is well-scoped for the domain of ground truth checking, including monitoring, pricing, compliance, and endpoint verification.
Completeness4/5
The tool set is comprehensive for the stated purpose, covering most common verification needs. Minor gaps might include the absence of a tool for checking social proof or user reviews, but overall the surface covers the core workflows (monitoring, pricing, compliance, security headers, package comparison, and claim verification).
Average 4.5/5 across 16 of 16 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- Last stable release on June 8, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, so the safety profile is clear. The description adds that it requires a team API key and lists the data fields returned, providing context beyond annotations without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two concise, front-loaded sentences with no wasted words. It efficiently delivers the core purpose and a key requirement (team API key).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the existence of an output schema (not shown but present), the description need not detail return values. It covers the tool's purpose, data included, and a critical prerequisite (team API key). With 100% schema coverage for parameters and a clear context of sibling tools, it is sufficiently complete for a read-only retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for both parameters (monitor_id, limit). The description does not add additional meaning beyond the schema; it only implicitly mentions temporal ordering but no extra parameter details. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Retrieve' and the resource 'most recent run results for a monitor', listing specific data included (change details, confidence score, evidence URLs, error info). It differentiates from siblings like list_monitors (which lists monitors) and run_monitor_now (which triggers a run).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use this tool versus alternatives. It implies usage when needing recent run results, but lacks exclusions or comparisons to sibling tools like generate_change_report or verify_claim.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds value by specifying that a team API key is required and that the output includes last run status and schedule. This provides context beyond the annotations, though it does not detail any potential limits or pagination.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences. The first sentence front-loads the key functionality, and the second adds a necessary requirement. There is no redundant or wasted text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple listing tool with one optional parameter, comprehensive annotations, and an existing output schema, the description covers all necessary aspects: purpose, returned data, and authentication requirement. No gaps are apparent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'active_only' is fully described in the input schema with a default value and explanation. The description does not add any extra semantics beyond what is already in the schema, so it meets the baseline for 100% schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List'), the resource ('monitors'), the scope ('owned by this API key'), and what is included ('with last run status and schedule'). This distinguishes it from sibling tools like 'get_monitor_result' which focuses on a single monitor's result.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions a prerequisite ('Requires a team API key') but does not explicitly guide when to use this tool versus alternatives like 'list_resources' or when not to use it. The context from sibling tools helps differentiate, but the description itself lacks comparative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide no readOnly or destructive hints, but the description adds valuable behavioral details: it records results, returns a change indicator, counts against monthly quota, and requires a team API key. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences with no wasted words. The first sentence clearly states the main action. It could be slightly more concise, but it is well-structured and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low parameter count and presence of an output schema, the description covers key aspects: usage context, quota impact, auth requirements, and result behavior. It is sufficiently complete for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already documents the single required parameter 'monitor_id'. The description does not add further semantic detail beyond what the schema provides, so 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool runs a monitor's verification check immediately outside its normal schedule. It uses specific verbs and resources, and effectively distinguishes itself from sibling tools like list_monitors or create_monitor.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates this tool is for immediate checks outside normal schedule and mentions quota impact, but does not explicitly state when not to use it or provide alternatives like get_monitor_result for past results.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations show readOnlyHint=false, destructiveHint=false. The description adds that it creates a persistent resource, runs automatically, and persists results, which is consistent and adds useful behavioral context beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with purpose, and every part is informative. No fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool creates a monitor with 6 parameters (3 required) and has an output schema, the description covers purpose, execution, storage, and API key requirement. It does not explain the output, but that's expected with an output schema. Minor missing details like error conditions are not critical.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with detailed parameter descriptions. The tool description summarizes target types and schedule but does not add significant new meaning beyond the schema, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Create' and the resource 'persistent monitor', and lists the various target types (URL, pricing page, etc.), distinguishing it from siblings like list_monitors, delete_monitor, check_endpoint, etc. It is specific and actionable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains scheduling (automatic via cron or on-demand via run_monitor_now), storage in SQLite, and the requirement for a team API key. It provides context for when to use the tool but does not explicitly contrast with sibling tools like check_endpoint for one-time checks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, destructiveHint, and idempotentHint, indicating a safe, read-only, idempotent operation. The description adds an important behavioral detail: it requires a team API key. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences that front-load the purpose and immediately follow with content and a requirement. Every sentence adds value with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists (not shown but indicated), the description does not need to explain return values. It mentions the content of the report but could explicitly reference the 'period' parameter to clarify the time window. Overall, it is mostly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 fully. The description does not add any additional semantics or context about the parameters, remaining at baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a summary report of monitor activity for a time window, listing specific content such as monitors run, changes, failures, risk levels, and follow-up actions. The verb 'generate' and the resource 'change report' are specific and distinct from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear use case (summary report for a time window) and mentions a prerequisite (requires team API key). However, it does not explicitly state when not to use this tool or suggest alternatives among siblings, so it is slightly less than perfect.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds value by stating 'Zero-cost schema discovery' and 'No quota consumption, no API key required', which are behavioral traits beyond annotations. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each serving a clear purpose: stating the action, highlighting value, and providing usage guidance. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters, rich annotations, and presence of output schema, the description adequately covers what the tool does. It mentions output (list of tools and access tiers), and the output schema can provide further detail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has zero parameters (100% coverage). Description doesn't need to add parameter details. Baseline for no parameters is 4, and no additional info is required.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'List all available Ground Truth tools and their access tiers', using a specific verb and resource. This distinguishes it from sibling tools like list_monitors (which lists monitors) and compare_competitors (which compares competitors).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Call this to explore what verification tools are available before making a tool call', providing clear context for when to use. Also mentions zero cost and no quota consumption. Could be improved by noting when not to use (e.g., for data retrieval), but overall clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly, idempotent. The description adds important caveats: it is a 'signal scanner, not proof of certification or legal sufficiency,' and lists signals scanned. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences that are front-loaded with purpose and usage, followed by a list of scanned signals and a caveat. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of output schema and annotations, the description is sufficiently complete: it covers purpose, when to use, what is scanned, and limitations. No gaps for an agent to misuse the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single 'url' parameter. The description adds meaningful context by specifying the type of URLs acceptable (public trust, security, compliance, policy pages) and listing what signals are scanned, enhancing the schema's basic description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool scans public compliance/trust pages for enterprise buying signals, listing specific items like SOC 2, ISO 27001. It distinctively targets compliance posture verification, differentiating it from siblings like check_endpoint or check_pricing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description specifies when to use: 'before you claim a vendor supports a particular compliance posture.' It also clarifies what it is not ('signal scanner, not proof'). However, it does not explicitly mention when not to use or provide alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds beyond annotations: specifies permanent deletion of results and auth requirement. Annotations give destructiveHint but description enriches with specifics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, zero fluff. Key information front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With output schema present, description covers purpose, irreversibility, auth. Sufficient for a destructive tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and monitor_id description matches tool context. Description adds no extra parameter meaning beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verb 'delete' and resource 'monitor and all its stored results', clearly distinguishing it from siblings like create_monitor, list_monitors.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
States 'cannot be undone' implies cautious use, and 'Requires a team API key' gives prerequisite. No explicit when-not or alternatives, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses return format (per-package registry metadata in input order), field variability by registry, handling of missing packages (found=false), and caching (5 minutes). Annotations already show read-only, non-destructive, idempotent, open-world; description adds useful context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Four sentences, front-loaded with purpose, then use cases, output behavior, and caching/exclusions. No wasted words, well-organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 2 parameters, output schema, rich annotations, and description covering inputs, outputs, edge cases (missing packages), prohibitions, and caching, the tool is fully specified for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers both parameters with descriptions; description reinforces with examples ('react', 'vue') and constraints (exact names, same registry). Since schema coverage is 100%, baseline is 3; description adds marginal extra meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'compare' and resource 'exact package names using live npm or PyPI metadata'. It differentiates from sibling tools like estimate_market by focusing on known packages, not discovery.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use (known candidates needing evidence for claims like newer, maintained, different licenses) and when not to use (discovery, market size, cross-registry). Provides clear exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint true, destructiveHint false, idempotentHint true, openWorldHint true. The description adds valuable behavioral context: output is heuristic and page-level, and clarifies what the tool does not do (mapping every price or normalizing regional billing). No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two sentences, both front-loaded with key information. Every word adds value—no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (not shown but indicated), the description adequately explains the tool's behavior, limitations, and usage. It is complete enough for an agent to invoke correctly without additional clarification.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
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. The description adds extra context about how the parameters are used (name as label in output, url as page URL) and the overall purpose, which enriches the agent's understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Compare') and resource ('public pricing pages') and clearly defines the scope (two to five, side-by-side). It distinguishes this tool from siblings such as 'compare_competitors' and 'check_pricing' by focusing on live, heuristic comparison of visible prices and signals.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use: 'Use this when you want a quick, live comparison...' and sets expectations by noting limitations (heuristic, page-level, does not map every price or normalize regional differences). It provides clear context for appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only and idempotent. The description adds valuable context: responses are cached for 5 minutes and results reflect search counts, not market share. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences with a clear front-loaded purpose, followed by usage guidelines and behavioral notes. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only two parameters, full schema coverage, and rich annotations, the description covers all necessary aspects: purpose, usage, limitations, and caching behavior. Output schema handles return value documentation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already fully documents both parameters. The description mentions 'live result counts plus representative matches' as output but does not add parameter-specific meaning beyond what's in the schema. Baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches npm or PyPI to estimate market crowdedness, with a specific verb ('Search') and resource (package registries). It distinctly differs from sibling tools that focus on compliance, security, or pricing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use (evaluating category crowdedness) and when not to use (comparing exact packages, inferring adoption from downloads). It also mentions caching behavior and provides context about what the tool reflects.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate safe, idempotent read. Description adds behavioral details: fetches URL, inspects specific headers, normalizes bare domains to HTTPS, and warns about limitations. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Multiple sentences but each serves a purpose: action, usage context, list of headers, and limitation. Front-loaded with the core action. No unnecessary words; efficient and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity (1 param, rich annotations, output schema exists), the description covers purpose, usage, limitations, and behavioral nuances completely. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already fully describes the 'url' parameter with high coverage (100%). Description does not add new semantics beyond usage context; it reinforces 'public URL' but not necessary. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool fetches a public URL and inspects security headers, listing specific headers. It distinguishes itself from siblings by focusing on quick due diligence for browser security, not replacing full reviews.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides clear when-to-use: before claiming a strong security baseline, for quick due diligence on public apps/docs. It also states limitations: does not replace real security review, authenticated testing, or vulnerability scanning. No explicit sibling alternatives, but context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnly, idempotent, openWorld hints. Description adds valuable behavioral context: returns cache information, results cached for 5 minutes, extracts heuristic labels and free signals, and warns about limitations. No contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is four sentences, front-loaded with main action, no unnecessary words. Each sentence adds essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given single parameter and existence of output schema, description fully covers what the tool does, returns, and its limitations. It is complete for an agent to decide and use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 100% of parameter description, providing clear guidance on URL format. Description reinforces the context of having a likely pricing URL. Additional advice on preferring specific pages over generic homepages adds value beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool fetches a public pricing page and extracts first-pass pricing signals. It specifies the specific verb 'fetch' and resource 'pricing page', and distinguishes from siblings like compare_pricing_pages by focusing on a single URL quick scan.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use this when you already have a likely pricing URL and need a quick live scan.' Lists what it does not do (map prices to exact plans, normalize currencies, etc.) and limitations (JS-rendered, logged-in pages may be missed).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behavioral traits: tests run in order with no branching, no early exit, and the verdict is mechanical. Annotations already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, which are consistent with the description. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (5 sentences) and well-structured, with the most important information front-loaded. Every sentence adds value: purpose, usage, behavioral details, and alternatives.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multi-step with multiple test types) and the presence of annotations and output schema, the description is complete. It covers the verdict logic, ordering constraints, and coordination nature, leaving no obvious gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds overall context about the coordination role and test execution logic, which helps the agent understand how to construct the 'tests' array, even though it doesn't add new details per parameter beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Run a small verification plan made of concrete live checks and summarize whether a hypothesis is supported.' It uses specific verbs ('run', 'summarize') and distinguishes from siblings like verify_claim, estimate_market, and compare_competitors.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly explains when to use this tool ('when one conclusion depends on multiple simple checks') and provides alternatives for other scenarios. It also clarifies what the tool is not ('not an open-ended research agent') and lists specific use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds further behavioral context by explaining it's unauthenticated, reports HTTP status, content type, elapsed time, auth/rate-limit signals, and a response sample, and notes that success only proves basic reachability at fetch time. This goes beyond annotations without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three well-structured sentences. The first sentence states purpose and when to use, the second explains what the tool reports, and the third provides a strong 'do not use' warning. Every sentence is essential and adds value, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter and an output schema), the description covers purpose, usage guidelines, behavioral details, and limitations comprehensively. It tells the agent when to use, what it returns, and what not to expect, making it fully sufficient for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter (url) with 100% description coverage. The description adds extra information beyond the schema by specifying that bare domains are accepted and normalized to https:// automatically, which helps the agent understand how to provide the URL.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool performs a live, unauthenticated fetch against a public URL or API endpoint, and specifies its purpose: to check reachability before recommending, documenting, or building on top of an endpoint. It distinguishes from siblings by focusing on simple reachability questions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says when to use the tool (before recommending, documenting, or building on top) and provides clear alternatives: it should not be used for authenticated flows, POST side effects, JavaScript execution, or deeper business logic. This gives the agent explicit guidance on when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, idempotentHint), the description adds the algorithm: case-insensitive keyword match, half-threshold for support, cache duration. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loaded with primary action, each sentence adds value, no unnecessary words. Length is appropriate for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers all necessary aspects: purpose, when to use, algorithm, caching. Given the presence of an output schema for return values, the description is complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 100% schema coverage, the description adds meaningful context: explains the matching is case-insensitive substring and suggests choosing phrases verbatim, and clarifies the half-keyword threshold for support.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies the verb 'check' and resource 'factual claim supported by evidence URLs', and distinguishes from siblings by explicitly stating it is not for open-ended search, semantic reasoning, or contradiction extraction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use ('evidence-backed claim checks on known pages') and when not to use ('not for open-ended search...'), with clear context about keyword matching and caching.
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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