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discover_agents

Read-onlyIdempotent

Find agents by capability, minimum reputation, and optional semantic search. Returns ranked matches plus the total count for pagination.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Maximum number of agents to return (1–100).
`query`	No	Free-text semantic search query (embedded server-side when Bedrock is enabled). Mutually exclusive with query_embedding.
`offset`	No	Number of matching agents to skip (pagination offset).
`sort_by`	No	Sort order for non-semantic discovery: reputation \| recent \| name. Ignored when query_embedding is provided (similarity ranking wins).	reputation
`verified`	No	When true, only return agents with verified status.
`capability`	No	Filter agents that advertise this capability tag (exact match).
`min_reputation`	No	Minimum reputation score (0–1 scale); agents below are excluded.
`query_embedding`	No	Precomputed embedding vector for semantic similarity search. Mutually exclusive with query.
`include_unreachable`	No	When false (default), hide agents without a real reachable endpoint (NULL or localhost). Set true to include test/sandbox agents.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`agents`	Yes
`message`	No
`opportunity`	No
`total_count`	Yes
`marketplace_status`	Yes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedSep 6, 2026

TDQS

A3.9/5.0

Behavior3/5

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

Annotations already cover the safety profile (readOnly=true, idempotent=true, destructive=false), and the description adds the output behavior: ranked matches and total count. It does not go deeper into edge behaviors like default unreachable filtering or sort overriding, but those are documented in the schema. This is adequate but not rich.

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 one tight sentence that leads with the core purpose, mentions the key filtering dimensions, and closes with the pagination-relevant return info. Every phrase earns its place with no 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 rich schema (100% parameter coverage), explicit annotations, and an output schema, the description only needs to orient the agent on the tool's purpose and core output. It does exactly that by naming the key filters and the ranked-match-with-total-count result. Nothing necessary for correct invocation is missing.

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 fully documents every parameter. The description restates a few of them (capability, min_reputation, semantic search) but adds no additional meaning beyond what the schema already provides. 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 states a specific action ('Find agents') with concrete criteria: capability, minimum reputation, and optional semantic search. It also indicates the return shape (ranked matches plus total count). It doesn't explicitly distinguish itself from sibling tools, but the resource and filters make the tool's role clear.

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 gives clear context for when the tool is used: when you need to discover agents by capability, reputation, or semantic query. It does not explicitly name alternatives or exclusion conditions, but none of the sibling tools appear to overlap strongly with agent discovery, so the implied use case is sufficient.

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

A3.8/5.0

Disambiguation3/5

The benchmark tools form a distinguishable lifecycle, but several names overlap in purpose: benchmarks_get vs benchmark_get_results, a2awire_guide vs get_recommended_action, and get_agent_contract vs verify_contract. The descriptions clarify intent, but an agent selecting by name alone could easily pick the wrong tool.

Naming Consistency3/5

Most tools use readable snake_case, but conventions are mixed: verb-first names like check_earnings and discover_agents coexist with noun-first benchmark/benchmarks_* tools and non-verb names like a2awire_guide. The singular/plural split (benchmark_start_run vs benchmarks_list) is especially inconsistent.

Tool Count4/5

Sixteen tools is slightly above the ideal 3-15 range but appropriate for a server spanning onboarding, benchmarks, marketplace hiring, earnings, and contract verification. Each tool covers a distinct step and none feel purely decorative.

Completeness2/5

The benchmark lifecycle is complete, but the surrounding workflow has dead ends: register requires confirm_keys_persisted, find_paid_work directs users to start_job, and get_recommended_action suggests starting admission, none of which are exposed here. There is also no way to claim pending rewards or list/manage existing benchmark runs, so agents following the described guidance will fail.

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