Agent Radar
Server Details
Search, inspect, recommend, and explain rated AI tools through Agent Radar.
- 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 3.8/5 across 4 of 4 tools scored.
Tools are mostly distinct but 'explain_rating' and 'get_tool_card' both operate on a single tool_id and return rating-related information, which could cause minor confusion. However, descriptions clarify their different outputs.
All tool names follow a consistent verb_noun pattern (explain_rating, get_tool_card, recommend_tools, search_tools), making them predictable and easy to understand.
With 4 tools, the set is well-scoped for the server's purpose of querying and recommending tool cards. Each tool has a clear role without unnecessary bloat.
The tool surface covers key operations: retrieving individual cards, explaining ratings, searching, and recommending. Minor gaps exist, such as batch retrieval or listing all tools, but search handles most needs.
Available Tools
4 toolsexplain_ratingExplain a tool ratingARead-onlyIdempotentInspect
Return rating dimensions, explanations, penalties, and boosts for one tool_id.
| Name | Required | Description | Default |
|---|---|---|---|
| tool_id | Yes | Stable Agent Radar tool identifier, usually obtained from search_tools results. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by specifying the exact components returned (dimensions, explanations, penalties, boosts), which the annotations do not convey. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that conveys all essential information without extraneous words. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema available, the description does not need to detail return values. The tool is simple (1 param, no nested objects), and the description covers the core behavior completely.
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 parameter tool_id has 100% schema description coverage, so the baseline is 3. The tool description does not add extra semantic detail beyond what is already in the schema (e.g., format or source).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Return' and explicitly names the resources: rating dimensions, explanations, penalties, and boosts. It also ties the output to a single tool_id, which distinguishes it from sibling tools like search_tools or recommend_tools that operate on multiple tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context (after obtaining a tool_id from search_tools) but does not explicitly state when to use this tool versus alternatives like get_tool_card. No exclusions or comparative guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_tool_cardGet an Agent Radar tool cardARead-onlyIdempotentInspect
Return one Tool Card and its Rating Result by stable tool_id.
| Name | Required | Description | Default |
|---|---|---|---|
| tool_id | Yes | Stable Agent Radar tool identifier, usually obtained from search_tools results. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds that it returns a Tool Card and Rating Result but no further behavioral context (e.g., error conditions, performance).
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?
Single sentence, front-loaded with core action, no redundant words. Perfectly 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?
With simple input, robust annotations, and presence of output schema, the description sufficiently covers the tool's functionality. No gaps identified.
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%. The param tool_id description adds context about origin from search_tools, so description need not compensate. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Return one Tool Card and its Rating Result by stable tool_id', specifying verb, resource, and retrieval method. Distinguishes from siblings like search_tools and recommend_tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use guidelines compared to siblings. The tool_id description implies obtaining from search_tools, but lacks direct comparison or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recommend_toolsRecommend toolsARead-onlyInspect
Recommend known tools for a task using request-scoped LLM credentials when required.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | Natural-language development task for which Agent Radar should recommend suitable AI tools. | |
| model | No | LLM provider model identifier recognized by Agent Radar. Omit it to use the server-configured or registry default model. | |
| top_k | No | Maximum number of recommended candidates to return, from 1 to 50. Defaults to 5 when omitted. | |
| budget | No | Natural-language cost constraint, such as free, free_or_low_cost, or a project-specific budget. | |
| environment | No | Execution contexts such as local development, CI, browser, cloud, IDE, or production. | |
| output_format | No | Preferred presentation format for recommendation content: json or markdown. MCP still returns structured content. | |
| existing_tools | No | Tools already available to the project or agent, used as compatibility and duplication context. | |
| risk_tolerance | No | Maximum preferred risk tolerance for the recommendation: low, medium, or high. | |
| language_or_stack | No | Programming languages, frameworks, runtimes, or other stack constraints relevant to the task. | |
| allowed_permissions | No | Permission scope names the caller is willing to allow; candidates outside these boundaries are treated conservatively. | |
| preferred_tool_types | No | Agent Radar tool types to prefer when selecting candidates. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint=true) already indicate non-destructive behavior, and description adds minimal extra context about LLM credentials. No contradictions, but no deeper behavioral detail like response structure or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (one sentence, 15 words) with no fluff. However, it could be more informative without sacrificing brevity, such as mentioning the output format or key parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 11 parameters, a single sentence is insufficient. It lacks context about output format, how to craft the task description, or when to use optional parameters. The output schema exists but is not explained in the description.
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 fully documents parameters. The description adds no additional semantics beyond the brief usage of LLM credentials, so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action ('Recommend known tools') and target ('for a task'). It distinguishes from siblings like search_tools by emphasizing recommendation and mentions usage of request-scoped LLM credentials.
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 the tool is for recommendation but provides no explicit guidance on when to use it versus siblings like search_tools, explain_rating, or get_tool_card. No exclusions or alternative suggestions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_toolsSearch Agent Radar toolsARead-onlyIdempotentInspect
Search Agent Radar Tool Cards by query text and optional type, tag, or risk filters.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Free-text tool name, capability, or task to search for. Use an empty string to browse the highest-rated matching catalog entries. | |
| top_k | No | Maximum number of results to return, from 1 to 50. Defaults to 5. | |
| filters | No | Optional exact-match filters applied before search ranking. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds the concept of filtering but no additional behavioral context beyond what annotations convey. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence (14 words) that is front-loaded and contains no fluff. Every word serves a 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 rich schema (100% coverage, output schema, annotations), the description is adequate but minimal. It omits mention of top_k (though schema covers it) and the browse behavior for empty queries (described in schema but not here). Sufficient but could be more 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?
Schema description coverage is 100% (all 3 parameters documented). The description mentions 'optional type, tag, or risk filters' which maps to the filters parameter but does not add meaning beyond schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it searches Agent Radar Tool Cards using query text and optional filters, distinguishing it from siblings like get_tool_card (retrieve specific card) and recommend_tools (recommendations).
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 (searching by text/filters) but does not explicitly state when not to use or contrast with siblings like get_tool_card or recommend_tools. No guidance on alternatives.
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
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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
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