Agent Radar

Recommend tools

recommend_tools

Read-only

Recommend known tools for a task using request-scoped LLM credentials when required.

Input Schema

TableJSON Schema

Name	Required	Description
`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

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

B3.4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, indicating a safe read operation. The description adds that it uses 'request-scoped LLM credentials when required,' which is useful but lacks detail on credential handling or side effects. It does not contradict 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 a single sentence, making it concise. It front-loads the core action ('Recommend known tools for a task') but adds a somewhat redundant clause about credentials. Could be slightly tighter, but overall efficient.

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 tool has 11 parameters, a rich schema, and an output schema, the description is minimal. It does not explain what 'known tools' means or provide context for the recommendation task. The output schema covers return values, so completeness is adequate but not thorough.

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 all 11 parameters described in detail. The description does not add additional meaning beyond 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's purpose: to recommend known tools for a task. It uses a specific verb ('Recommend') and resource ('tools'), and the action is distinct from its siblings (explain_rating, get_tool_card, search_tools).

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 provides no guidance on when to use this tool versus alternatives like search_tools or get_tool_card. The mention of 'request-scoped LLM credentials' hints at a specific context but does not compare or contrast with sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4/5.0

Disambiguation5/5

Each tool targets a distinct operation: explain_rating for detailed rating breakdown, get_tool_card for full card retrieval, recommend_tools for task-based suggestions, and search_tools for querying the database. No overlapping purposes.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (explain_rating, get_tool_card, recommend_tools, search_tools) using snake_case, making the set predictable.

Tool Count5/5

With only 4 tools, the server is tightly scoped to cover core operations (search, retrieve details, explain ratings, recommend) without excess or deficiency.

Completeness5/5

The tool set covers the primary needs of a tool registry: searching, retrieving individual cards, explaining ratings, and getting recommendations. There are no obvious gaps given the stated purpose.

Recommend tools

Input Schema

Output Schema

TDQS

Glama MCP Gateway

TDQS

Resources