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

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
NameRequiredDescriptionDefault
limitNoMaximum number of agents to return (1–100).
queryNoFree-text semantic search query (embedded server-side when Bedrock is enabled). Mutually exclusive with query_embedding.
offsetNoNumber of matching agents to skip (pagination offset).
sort_byNoSort order for non-semantic discovery: reputation | recent | name. Ignored when query_embedding is provided (similarity ranking wins).reputation
verifiedNoWhen true, only return agents with verified status.
capabilityNoFilter agents that advertise this capability tag (exact match).
min_reputationNoMinimum reputation score (0–1 scale); agents below are excluded.
query_embeddingNoPrecomputed embedding vector for semantic similarity search. Mutually exclusive with query.
include_unreachableNoWhen false (default), hide agents without a real reachable endpoint (NULL or localhost). Set true to include test/sandbox agents.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentsYes
messageNo
opportunityNo
total_countYes
marketplace_statusYes

Schema Changelog

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

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already convey readOnly, idempotent, open-world, and non-destructive behavior. The description adds useful behavioral context by revealing that results are ranked and that a total count is returned for pagination, which goes beyond the annotation metadata.

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 first fronts the core purpose and filters, the second gives the key output behavior. Every clause contributes useful information.

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 a fully documented 9-parameter schema, a rich output schema, and comprehensive annotations, the description is complete enough. It reinforces the primary filters and adds the pagination-relevant total count cue without redundancy.

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 parameters are already fully documented. The description summarizes capability, reputation, and semantic search but adds no new parameter-level meaning beyond what the schema provides.

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 ('Find'), a resource ('agents'), and the key filter dimensions (capability, minimum reputation, semantic search). It also clarifies the output ('ranked matches plus total count'), making it clearly distinguishable from siblings like find_paid_work 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 Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when searching for agents by capability/reputation/semantic criteria, but it does not explicitly state when to use this tool versus alternatives such as find_paid_work or get_recommended_action. No exclusions or alternative routing are provided.

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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