FDA Recall Alerts — Food & Product Safety (fdarecall)

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 5, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds useful behavior beyond that by describing ranked matches and total count for pagination, which signals how results are ordered and how to page through them. It does not go into deeper edge cases, but the annotations lower the bar for disclosure.

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?

A single sentence that front-loads the verb and resource, lists the key filters, and closes with the return behavior. There is zero wasted wording, and it is immediately scannable.

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 100% schema coverage and the presence of an output schema, the description is complete enough for an agent to correctly select and invoke the tool. It captures the essence of discovery, ranking, and pagination without needing to enumerate all nine parameters or the result structure.

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 mentions capability, minimum reputation, and optional semantic search, which map to existing parameters but adds no information beyond what the schema already provides. Other parameters like limit, offset, verified, and include_unreachable are left entirely to 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 uses a specific verb ('Find') with a clear resource ('agents') and states the main selection criteria: capability, minimum reputation, and optional semantic search. This is enough to distinguish it from sibling tools like get_agent_contract or find_paid_work, since the target resource is unambiguously agents.

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 clearly implies when to use it: whenever the agent needs to discover or search agents by filters. It does not explicitly name alternative tools or state when NOT to use this tool, so it falls short of a 5, but the context is clear enough to guide selection against the listed siblings.

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

All FDA Recall Alerts — Food & Product Safety (fdarecall) tools Inspect

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TDQS

B3.3/5.0

Disambiguation2/5

The tool set blends FDA recall query tools with generic A2AWire marketplace tools, making purpose boundaries unclear. Multiple tools overlap: data_session_fund, data_session_funding_package, and data_session_open all describe payment/session setup, while a2awire_guide and get_recommended_action both serve as navigational helpers.

Naming Consistency2/5

Some tools follow a verb_noun pattern (data_session_open, data_session_query, discover_agents), but others are inconsistent or vague (a2awire_guide, check_earnings, register, verify_contract). The mix of domain-specific and platform tool naming with no coherent convention makes the surface feel disjointed.

Tool Count2/5

16 tools is not inherently excessive, but most are unrelated to FDA recall alerts; they cover agent registration, onboarding, escrow, hiring, and earnings. Only a handful actually concern the stated FDA recall data domain, so the count is poorly scoped for the server's apparent purpose.

Completeness2/5

For an FDA recall alert server, the surface is severely incomplete: there is no direct recall listing, search, filtering, or detail tool, only a generic data_session_query with preview. The remaining tools are platform infrastructure, leaving the actual recall domain shallow and dependent on an opaque paid query flow.

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