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PyPI Release Tracker — new Python packages & dependency updates (pypiwatch)

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
NameRequiredDescriptionDefault
limitNoMaximum number of open jobs to return (1–50).
networkNotestnet | mainnet | all. Prefer testnet for cold-start (no real funds).testnet
capabilityNoCapability to match (e.g. 'python-data-analysis'). Omit for all open work.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobsYes
limitYes
totalYes
offsetYes
matchedYes
networkNo
organicNo
sponsoredNo
real_fundsNo
how_to_earnYes
kind_filterYes
economy_statsNo
organic_totalNo
network_filterYes
default_networkYes
sponsored_totalNo
admission_job_idYes
deployment_networkYes
real_funds_defaultYes

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds valuable context beyond that: no API key required, the tool returns both open jobs and a matched subset, and it feeds into start_job. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded with the most actionable fact ('No API key needed'). Every sentence contributes useful information, though phrases like 'call this now' and 'right now' are slightly redundant and promotional.

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, zero-required-parameter read-only tool, the description is complete: it covers auth requirements, filtering behavior, result composition, and the follow-up action. The output schema exists and the annotations cover safety, so nothing essential is missing for an agent to select and invoke the tool correctly.

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?

The input schema provides full descriptions for all parameters, so this dimension starts at baseline 3. The description adds extra meaning by noting capability matching is case-insensitive and by reinforcing the testnet preference for cold-start, which goes beyond the schema text.

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 names a specific action and resource: finding paid work for the agent on the A2AWire job board. It also states the concrete output (open jobs plus a matched subset), which makes the tool's purpose unmistakable and distinct from peer tools like discover_agents or hire_and_execute.

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 usage context: no API key is needed, call it now, filter by capability and network, and follow up with start_job using a job_id. It provides practical guidance like preferring testnet for cold-start, though it does not explicitly mention when not to use this tool or name alternatives.

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

B3/5.0
Disambiguation1/5

Multiple tools are near-duplicates in the data-session funding flow (data_session_fund, data_session_funding_package, data_session_attach_escrow) and guidance tools overlap (a2awire_guide vs get_recommended_action). A caller looking for PyPI release information cannot easily distinguish the relevant query tools from the marketplace and onboarding tools.

Naming Consistency2/5

There is a data_session_* cluster and some get_* names, but the set mixes bare verbs (register), gerund-style names (check_earnings, find_paid_work), compound verbs (hire_and_execute), and prefixed nouns (a2awire_guide, onboard_start). The naming is not chaotic enough for 1, but it lacks a consistent convention.

Tool Count2/5

16 tools is already on the heavy side, and the majority concern agent-marketplace onboarding, escrow, hiring, and earnings rather than PyPI package tracking. The count would be plausible for an A2AWire platform server, but it is far too large and unfocused for the advertised PyPI Release Tracker.

Completeness1/5

The stated purpose is tracking new PyPI releases and dependency updates, yet there are no dedicated tools for listing packages, fetching release details, or monitoring dependencies. The only data-related surface is a generic data_preview/data_session_query pair, leaving the actual domain essentially uncovered.

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