mcp-gauge

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose: catalog vs preview, free raw data vs specialized triangles for different domains (crop, filing, grid, route), region bundle, and sample. No two tools seem to overlap.
Naming Consistency4/5
Tool names follow a consistent 'gauge_<domain>' pattern with snake_case. Minor inconsistency: 'gauge_catalog' and 'gauge_preview' both are listing tools, but the naming is mostly predictable.
Tool Count5/5
Ten tools is well-scoped for a specialized data server covering multiple domains (hydrology, air quality, agriculture, grid, shipping, regulatory). Each tool earns its place.
Completeness4/5
The tool surface covers major signals from the catalog, but some signals (e.g., standalone air quality) only appear in bundles, and there are no tools for updating data. Overall, it's complete for a read-only data retrieval server.
Average 3.9/5 across 10 of 10 tools scored. Lowest: 3.2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 13 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full burden. It discloses cost, payment mechanism (x402 on Base), data sources (USGS/NOAA/EPA/CAMS), and output components (band, distance-to-action, anomaly, record_hash). However, it omits potential side effects (e.g., is it read-only?), error behavior, rate limits, or confirmation that data is fetched externally.
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 two sentences with no wasted words. Front-loads the core function and then adds supplementary information. Could be slightly more structured (e.g., bullet points) but is efficient and clear.
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 no output schema, the description covers key outputs (thresholds, anomaly, hash) and constraints (one signal+entity, cost, supported signals). It does not detail response format or error states, but provides enough context for an agent to decide if this tool fits the task.
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 parameters are already documented. The description adds examples of signal_id and entity values, and mentions 'gauge_catalog' for entity lookup, but does not substantially enhance parameter meaning beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it provides a verifiable flood-risk/anomaly record for a single signal+entity, comparing current data to official thresholds and seasonal anomalies. It specifies supported signal types (hydrology, air, precip) which distinguishes it from broader tools, but does not contrast with sibling tools like gauge_catalog or gauge_preview explicitly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes that the tool costs $0.05, implying it's for paid verifiable records, but offers no explicit guidance on when to use this tool versus alternatives or when not to use it. No when-to-use or when-not-to-use statements are present.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the cost ($0.10 USDC) and outlines the computed components (demand, renewables, inflation, cross-validation). However, it does not describe output format, data source recency, error handling, or whether the operation is read-only (likely but unstated). The lack of output schema compounds this gap.
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 compact (three sentences) and front-loaded with the main concept. It includes cost and examples without unnecessary details. Every sentence contributes value, though it could be slightly more structured (e.g., separate output description).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no output schema and moderate complexity (computing a composite metric), the description omits important details: it does not specify the return format (JSON, fields, data types), how to handle invalid loc values, or whether results are cached or real-time. The target audience is mentioned, but usage context is incomplete for a tool with no structured output 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?
There is only one parameter (loc) with 100% schema coverage. The description adds context by giving example values (us-ercot, us-caiso, eu-germany) and stating it's a grid region id. Since schema coverage is high and description adds some examples, a 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 explicitly states the tool's purpose: computing a 'Power grid triangle' combining demand pressure, renewable resources, energy inflation, and cross-validation for a grid region. It identifies the target audience (power/energy & macro/inflation traders, utilities) and provides example locations, clearly distinguishing it from sibling tools covering different domains like crop drought or flood risk.
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 indicates the tool is for traders and utilities interested in grid stress and energy inflation, and gives example loc values. However, it does not explicitly state when to use or avoid this tool relative to alternatives, nor does it specify prerequisites or typical use cases beyond the broad audience statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It mentions the cost ($0.10 USDC) and the output (air, rain, river, narrative) but does not disclose other behavioral traits like rate limits, idempotency, or whether the call is destructive. The absence of such details leaves gaps for a tool with significant implications.
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 concise sentences. The first sentence clearly states the bundle composition and narrative, and the second provides cost and location guidance. Every word is relevant, and the structure is efficient for quick comprehension.
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 has a single parameter and no output schema, the description adequately explains what the tool returns (air, rain, river, narrative) and mentions cost and region format. It references gauge_preview for region lookup, which is helpful. Minor missing details about output format or data units, but overall sufficient for a low-complexity 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?
The schema already describes the 'loc' parameter with an example. The description adds a second example ('us-chicago') and references gauge_preview for valid regions, which is moderately helpful. However, since schema coverage is 100%, the description does not dramatically increase semantic understanding beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that this tool provides a bundle combining air quality, precipitation, and nearby river data for a US city, along with a narrative. It identifies the resource and its components, though it does not explicitly differentiate from sibling tools like gauge_river_free or gauge_preview.
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 implies when to use (for a three-leg bundle) and suggests checking gauge_preview for valid region identifiers. However, it lacks explicit guidance on when not to use this tool or how it compares to alternatives such as gauge_crop_drought or gauge_flood_risk.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so description carries full burden. It lists returned data types but does not mention error handling, authentication, or rate limits. The 'No payment' note adds some transparency.
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 a single focused sentence with an example, front-loading the core purpose 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?
For a simple 2-param tool with no output schema or annotations, the description adequately explains what the tool returns (current/previous/change/trend/record_hash) and provides an example, but lacks detail on output format and error states.
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% with descriptions. The description adds an example and clarifies the entity parameter as USGS site id, but does not significantly expand 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 clearly states it provides free raw US river readings (gage height/streamflow) for a USGS site id, with an example. It distinguishes from sibling tools that focus on other gauge-related data.
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 implies use for river level or streamflow data but does not explicitly state when to use this tool vs alternatives like gauge_flood_risk or gauge_crop_drought.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It mentions 'All official SEC EDGAR' and the cost, but does not disclose potential error conditions, data freshness, rate limits, or authentication requirements. This is adequate but leaves some 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph that efficiently conveys the tool's purpose, components, users, and cost. Every sentence adds value, though it could be slightly more structured (e.g., bullet points) without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema and minimal annotations, the description adequately explains the tool's inputs and intended analysis. However, it does not describe what the tool returns (e.g., a report, JSON, or visualization), leaving the agent to infer the output from the purpose.
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?
The only parameter 'entity' is described in the schema as 'ticker from watchlist' with examples. The description reinforces this with 'entity = ticker e.g. AMC, CVNA, GME' and references another tool, adding useful context 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 identifies the tool as a regulatory-filing triangle combining 8-K, NT late-filing, and Form 4 insider transactions with cross-validation. It specifies the intended users (event-driven, activist, etc.) and distinguishes it from sibling tools which cover unrelated domains like crop drought or flood risk.
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 states the target users and cost, but does not explicitly state when not to use this tool or provide direct comparisons to sibling tools. However, sibling tools are for different domains, so the context implies appropriate use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the tool is free and provides preview data, but does not mention idempotency, read-only nature, or any side effects. It is adequate but not thorough.
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 only two sentences, each concise and informative. It is front-loaded with the key offerings and ends with 'Free.' No superfluous words; every part earns its place.
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 no parameters and no output schema, the description covers the tool's output: coverage counts, pricing, and bundle lists. It does not detail return format or pagination, but for a free preview tool with zero inputs, this is largely sufficient. A small gap but 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?
The input schema has no parameters, so schema coverage is 100%. The baseline score is 3. The description adds value by explaining what the tool offers without parameters, but does not need to elaborate on parameter semantics. Scoring 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 provides 'GAUGE coverage counts, à-la-carte pricing menu, and the lists of region/company bundles.' It uses specific verb+resource and distinguishes from siblings by noting it is free and for discovering what to buy. This makes the purpose unmistakable.
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 advises 'Use this to discover what to buy,' providing clear context. It does not explicitly state when not to use it or list alternatives, but the sibling tool names imply other tools are for specific risk assessments. The guidance is adequate but lacks explicit 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?
No annotations exist, so description carries full burden. It discloses monetary cost ($0.10 USDC on Base) and lists data sources (WMO, AIS). It implies a read-only analytical tool with cross-validation. 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense sentence listing multiple components. While information-rich, it lacks paragraph breaks or bullet points, making it harder to parse quickly.
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?
Despite no output schema, the description explains the multifaceted outputs (disruption, cause, throughput, validation) and cost. It is sufficient for an agent to understand the tool's purpose and data, though error behavior and result format are omitted.
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 sole parameter 'loc' is fully described in the schema with examples. The tool description repeats the examples but adds no new syntax or format details. Schema coverage is 100%, so baseline 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 tool assesses 'logistics-flow disruption' with specific components (WMO sea state, wave decomposition, AIS throughput). It distinguishes from siblings like gauge_crop_drought or gauge_grid_stress by focusing on maritime chokepoints and shipping.
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?
Target users are specified ('shipping lines, commodity/freight traders, ports, marine insurers') and example chokepoint IDs (malacca, suez) provide clear context. No explicit when-not-to-use or alternatives, but sibling diversity makes context sufficient.
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?
No annotations exist, so description carries full burden. It accurately portrays a read-only listing without side effects, which is sufficient for a catalog tool. No contradictions or misleading statements.
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?
Single sentence with a front-loaded verb and resource, but the long list of categories makes it somewhat verbose. Still efficient for the information conveyed.
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 zero-parameter free catalog tool with no output schema, the description explains what the tool lists and hints at the response content ('entities you can query'). It is complete for its intended simplicity.
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?
With zero parameters and 100% schema coverage, the description does not need to add parameter details. The baseline for no parameters is 4, and the description appropriately focuses on output content.
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 GAUGE signals' and enumerates specific categories (hydrology, air quality, etc.), making the purpose unambiguous. It distinguishes from sibling tools like gauge_crop_drought by offering a comprehensive overview rather than a specific signal type.
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 implies use to see available signals before using specific tools, especially with 'Free, no payment' encouraging exploration. However, it does not explicitly state when to avoid it or mention alternatives, leaving some ambiguity.
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?
No annotations provided, so description must cover behavioral traits. It discloses cost ($0.10 USDC on Base), data sources (NOAA satellite VHI, USDM), and the four analytical components. However, it does not describe the output format, rate limits, or authentication requirements, leaving some ambiguity about how the tool behaves at runtime.
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 (2-3 sentences), front-loads the core purpose, and includes essential details (cost, data sources, examples) without unnecessary words. Every sentence adds value.
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's complexity (four analytical components) and lack of output schema, the description provides a good overview of what the tool returns (soil moisture, heat/GDD, vegetation health, cross-validation). However, it could improve by explicitly describing the output format (e.g., numeric indices, categorical status) and any prerequisites for use (e.g., API key).
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% and the parameter 'loc' is described with examples ('us-iowa, ar-pampas, ua-ukraine') both in the schema and the main description. The description adds real-world context by listing grain region examples, which helps the agent understand valid inputs 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?
Description clearly states it gauges agricultural drought for a grain region, listing four specific data components (soil moisture, heat/GDD, vegetation health, cross-validation). Distinguishes from sibling tools like gauge_flood_risk or gauge_grid_stress by focusing on crop-specific drought analysis. Verb is implicit in 'gauge' and the title 'Agriculture triangle: crop drought' reinforces the purpose.
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?
Target audience ('For ag traders & crop insurers') is explicitly mentioned, and the description implies usage for monitoring drought impact on crops. However, no explicit guidance on when not to use this tool or how it compares to siblings like gauge_flood_risk or gauge_river_free, which might also be relevant for agricultural analysis.
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?
With no annotations, the description carries the burden of describing behavior. It discloses that the sample contains current reading vs thresholds, anomaly data, and provenance. However, it doesn't mention auth requirements or read-only nature, which is fine for a sample.
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 a single, front-loaded sentence that conveys all necessary information without any wasted words. It efficiently explains the tool's purpose and content.
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 no parameters, no output schema, and is a simple sample, the description is complete. It explains what the sample contains, why it exists, and how it relates to paid tools.
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?
The tool has zero parameters, and the schema coverage is 100% (empty). Per guidelines, baseline is 4. The description adds no parameter info because none exist.
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 provides a free sample GAUGE record, showing exactly what a paid response contains. It uses specific verbs and resources, and distinguishes from paid versions and sibling tools like gauge_preview.
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 implicitly tells when to use: 'Try before you buy' indicates it's for evaluation. While it doesn't explicitly exclude alternatives, the purpose is clear enough for the agent to decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![mcp-gauge MCP server](https://glama.ai/mcp/servers/CHANGCHINFU/mcp-gauge/badges/card.svg)](https://glama.ai/mcp/servers/CHANGCHINFU/mcp-gauge)

Score Badge

Copy to your README.md:

[![mcp-gauge MCP server](https://glama.ai/mcp/servers/CHANGCHINFU/mcp-gauge/badges/score.svg)](https://glama.ai/mcp/servers/CHANGCHINFU/mcp-gauge)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/CHANGCHINFU/mcp-gauge'

If you have feedback or need assistance with the MCP directory API, please join our Discord server