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by io.github.pipeworx-io

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Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations declare idempotentHint true and destructiveHint false, but the description adds valuable context: storage is key-value scoped by identifier, persistent for authenticated users, 24-hour retention for anonymous. This goes beyond annotations without contradiction.

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 concise (four sentences) and front-loaded with the core purpose. Every sentence adds value—purpose, usage guidance, persistence details, and sibling references—without redundancy.

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 tool with two parameters and no output schema, the description is complete. It explains when to use, what is stored, scope, persistence behavior, and associated tools, leaving no critical gaps.

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 coverage is 100% with clear descriptions for both 'key' and 'value' parameters. The description reinforces key-value pairs with examples but does not add new syntax or constraints beyond the schema, so baseline 3 is appropriate.

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 clearly states the tool's purpose: 'Save data the agent will need to reuse later'. It provides concrete examples (resolved ticker, target address, user preference) and distinguishes from sibling tools like recall and forget, making its role unambiguous.

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 explicitly advises when to use: 'when you discover something worth carrying forward'. It also mentions pairing with recall and forget, providing clear usage context. It does not explicitly list when not to use, but the guidance is sufficient.

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

A3.8/5.0
Disambiguation3/5

Most tools understandably fall into distinct clusters (BLS data, Polymarket, entity research, memory, subscriptions) and have detailed descriptions, but there is real overlap among the query entry points: ask_pipeworx and ask_pipeworx_beta are currently identical, and ask_pipeworx, deep_research, and validate_claim can all answer similar factual questions. The descriptions help an agent choose, but the set still contains more than a couple of near-duplicate paths.

Naming Consistency3/5

All names are lowercase snake_case and several clusters share domain prefixes like bls_, polymarket_, and pipeworx_, which keeps the surface readable. However, the semantic naming pattern is mixed: verb+noun names like resolve_entity and list_subscriptions coexist with noun phrases like entity_profile, recent_alerts, and bls_latest, plus brand-led names like ask_pipeworx and polymarket_edges.

Tool Count2/5

At 36 tools, the set is well past the 25+ threshold for a heavy tool surface, and several tools inflate the count: duplicate ask_pipeworx variants, multiple overlapping Polymarket scanners, and one-off meta helpers. The broad Pipeworx scope explains some of the breadth, but the redundancy makes the set feel bloated.

Completeness4/5

The set covers its core workflows well: data lookup, grounded verification, entity profiling and comparison, BLS series access, Polymarket research, subscriptions, memory, and feedback. Minor gaps remain, such as no subscription-editing tool, no dedicated citation-reader tool, and no general web-search tool, but ask_pipeworx acts as a catch-all router that lets agents work around most of them.

Resources