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

data_session_attach_escrow

Idempotent

Buy per-query access to live data listings — first taste free via data_preview. Requires an agent API key (Authorization: Bearer or X-API-Key). Attach a buyer-funded proof escrow (open_tx_hash preferred, or proof_escrow_id) to an opened data session. Not guest-callable. REST: POST /api/v1/data-sessions/{session_id}/attach-escrow.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
session_idYesUUID of a data session you opened (from data_session_open).
open_tx_hashNo
proof_escrow_idNo

Schema Changelog

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

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=false, idempotentHint=true, and destructiveHint=false; the description adds value beyond them: the auth mechanism (Authorization: Bearer or X-API-Key), the guest-callability restriction, and the financial consequence of the operation ('Buy', 'buyer-funded'). There is no contradiction between the mutating description and the 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?

Four compact sentences, each carrying distinct information: business purpose, auth requirement, core action, and transport/restriction. There is no filler or repetition of schema content. The core action sentence appears third rather than first, but the opening framing is informative enough that this is a minor structural quibble.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Coverage of auth, transport, and exclusions is strong, but with no output schema, the description doesn't say what the call returns or what happens after escrow attachment. It also doesn't clarify how attach-escrow differs from the sibling tools data_session_fund and data_session_funding_package, which an agent could plausibly confuse. These gaps matter for a paid, state-changing operation.

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 only 33% (only session_id is documented in the schema). The description compensates by explaining the roles of the optional parameters — 'buyer-funded proof escrow (open_tx_hash preferred, or proof_escrow_id)' — and, crucially, signals that open_tx_hash is preferred over proof_escrow_id, a selection heuristic the schema lacks. It doesn't explain how to obtain either identifier, but the preference guidance is genuinely useful.

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 action — 'Attach a buyer-funded proof escrow (open_tx_hash preferred, or proof_escrow_id) to an opened data session' — with a clear verb and resource, plus the business purpose ('Buy per-query access to live data listings'). It names data_preview as the free alternative, which helps differentiate it from siblings like data_session_open and data_session_query. The REST endpoint reinforces the scope.

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?

It provides clear context for use (after opening a session, to buy per-query access) and explicitly names data_preview as the free alternative. Prerequisites are stated ('Requires an agent API key') and an exclusion is given ('Not guest-callable'). It stops short of a full when/when-not map because it never contrasts with the closely related sibling data_session_fund.

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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