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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

A4.9/5.0
Behavior5/5

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

Adds key context beyond annotations: key-value scoped by identifier, persistent for authenticated users, 24h retention for anonymous. No contradictions.

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?

Three concise sentences, front-loaded with main purpose. Every sentence adds value—usage guidance, persistence behavior, and pairing with other tools.

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 2 parameters and no output schema, the description fully covers purpose, when to use, persistence details, and relationship with recall/forget.

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 coverage is 100%, so baseline 3. Description enhances with examples of key patterns (e.g., 'subject_property') and value descriptions (findings, addresses, preferences).

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?

Description clearly states the tool saves data for reuse across conversations/sessions, with specific verb 'save' and resource 'data'. Distinguishes from siblings recall and forget.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

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

Explicitly says when to use: 'when you discover something worth carrying forward'. Provides examples and pairs with recall/forget for complete workflow.

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

A3.9/5.0
Disambiguation3/5

Most tools have distinct, well-scoped purposes, but several question-answering/research tools sit close together: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim can all be selected for factual questions. The descriptions are detailed enough to reduce ambiguity, but ask_pipeworx_beta is currently identical to ask_pipeworx, and discovery helpers like discover_tools, suggest_questions, and pipeworx_trending also overlap somewhat.

Naming Consistency4/5

Names are uniformly snake_case and mostly follow a verb_noun pattern such as build_url, list_subscriptions, resolve_entity, and validate_claim. The polymarket_* and pipeworx_* prefixes form a readable convention, though a few names like pipeworx_feedback and polymarket_arbitrage are noun-phrases rather than verb-first actions.

Tool Count2/5

34 tools is well past the 25+ threshold where even a broad platform starts to feel bloated. The set mixes data research, prediction-market tooling, URL utilities, memory, subscriptions, feedback, and npm scanning, which would be more coherently split across focused servers.

Completeness3/5

The core research workflows are thoroughly covered: routing, grounded answers, deep research, entity resolution, comparisons, claim validation, discovery, alerts, and memory all exist. However, the URL utility and dependency-scanning side domains feel tacked on and incomplete, and there is no dedicated tool to fetch an arbitrary pipeworx:// citation record even though such URIs are returned throughout.

Resources