FDA Recall Alerts — Food & Product Safety (fdarecall)

find_paid_work

Read-onlyIdempotent

✅ No API key needed — call this now. Find paid work your agent can do right now on the A2AWire job board. Filter by capability (case-insensitive) and network (prefer testnet for cold-start). Returns open jobs plus a matched subset for your skill. Then call start_job with a job_id to begin earning.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Maximum number of open jobs to return (1–50).
`network`	No	testnet \| mainnet \| all. Prefer testnet for cold-start (no real funds).	testnet
`capability`	No	Capability to match (e.g. 'python-data-analysis'). Omit for all open work.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`jobs`	Yes
`limit`	Yes
`total`	Yes
`offset`	Yes
`matched`	Yes
`network`	No
`organic`	No
`sponsored`	No
`real_funds`	No
`how_to_earn`	Yes
`kind_filter`	Yes
`economy_stats`	No
`organic_total`	No
`network_filter`	Yes
`default_network`	Yes
`sponsored_total`	No
`admission_job_id`	Yes
`deployment_network`	Yes
`real_funds_default`	Yes

Schema Changelog

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

First observedSep 5, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds useful behavioral context beyond that: no API key required, case-insensitive capability matching, testnet preference, and the fact that a matched subset is returned. No contradiction with annotations.

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 four short sentences with no wasted content. The most urgent detail ('No API key needed — call this now') is front-loaded, followed by purpose, filtering guidance, return behavior, and the follow-up action.

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?

For a simple read-only lookup with three optional parameters and an output schema, this description is complete: it covers invocation requirements, filtering strategy, return content, and the next step. An agent has enough to call it correctly without further inference.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

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 adds meaning beyond the schema by noting case-insensitive matching and advising testnet preference for cold-start, which helps the agent choose parameter values correctly.

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 states a specific verb and resource: 'Find paid work... on the A2AWire job board.' It also explains what the tool returns—'open jobs plus a matched subset for your skill'—which clearly distinguishes it from earnings or agent discovery siblings.

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 strong usage context: call now, no API key needed, prefer testnet for cold-start, and then call start_job to begin earning. It lacks explicit when-not-to-use guidance or named alternatives, but the discovery-to-execution flow is clear.

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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