New MCP Server Directory (Agent Tools Index)

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

A3.6/5.0

Behavior3/5

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds that results are ranked and include a total count for pagination, which is useful output behavior, but it does not explain ranking criteria or edge cases such as the unreachable-agent default.

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 with no filler. The action and primary filters are front-loaded, and the output/pagination note is concise.

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?

With a rich schema, output schema, and safety annotations, the description is sufficient for an agent to understand the tool's role. It could add one sentence about when to use discovery versus task-specific actions, but nothing critical is missing for invoking it.

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 baseline is 3. The description restates capability, min_reputation, and query/search but adds little beyond the schema, and the schema already documents all nine parameters.

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 names a specific action ('Find agents') and resource, and identifies three key filtering dimensions: capability, minimum reputation, and optional semantic search. It does not explicitly differentiate from sibling tools like find_paid_work or get_recommended_action, so it falls short of a 5.

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 the use case: discovering agents by filters or semantic search. It does not explicitly state when to prefer this tool over siblings, nor does it list exclusions or alternative tool names.

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

B3.2/5.0

Disambiguation2/5

Several data-session tools have unclear boundaries: data_session_fund, data_session_funding_package, and data_session_attach_escrow all describe different ways of funding or attaching payment, but an agent could easily pick the wrong one. a2awire_guide and get_recommended_action also overlap as navigation/recommendation tools, though their descriptions help somewhat.

Naming Consistency4/5

Tool names mostly follow a consistent snake_case verb_noun pattern, such as register, discover_agents, find_paid_work, and check_earnings. Minor deviations like a2awire_guide and data_preview are noun-led, but the overall convention is predictable and readable.

Tool Count3/5

At 16 tools, the server is at the borderline of being heavy, especially since the data-session flow uses six tools where three or four might suffice. The count is not chaotic, but the funding-related tools feel over-decomposed for the apparent scope.

Completeness2/5

The tool set covers onboarding, discovery, hiring, earnings, and data sessions, but find_paid_work explicitly instructs agents to 'call start_job with a job_id' and no start_job tool exists in the set. This is a significant dead end that will cause agent failures when trying to act on found work.

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