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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

A4.7/5.0
Behavior5/5

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

Discloses beyond annotations: key-value pair scoped by identifier, persistent memory for authenticated users, 24-hour retention for anonymous. Idempotent hint is consistent with description of storing by key.

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?

Concise paragraph, front-loaded with purpose. Every sentence adds value (use cases, pairing, retention). No wasted words.

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?

Complete for a simple key-value store: explains scope, retention, and relationship to recall/forget. No output schema needed.

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 already covers both parameters with examples. Description adds little beyond schema's own descriptions. 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?

Clearly states the tool saves data for reuse, with concrete examples (resolved ticker, target address, etc.). Distinguishes from siblings 'recall' and 'forget' by mentioning pairing.

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 describes when to use: 'when you discover something worth carrying forward'. Mentions pairing with recall and forget, providing clear context for 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

A3.9/5.0
Disambiguation2/5

Multiple tools appear to do nearly the same thing, notably ask_pipeworx and ask_pipeworx_beta (the description explicitly says they currently match exactly), plus ask_pipeworx_grounded and deep_research which are all variations of the same routing/query capability. The Polymarket tools and the AI-visibility tools also have overlapping boundaries, making it easy for an agent to pick the wrong one.

Naming Consistency4/5

Most tools follow a readable snake_case verb_noun pattern like ask_pipeworx, compare_entities, resolve_entity, scan_dependency, and validate_claim. There are minor deviations such as entity_profile, bet_research, pipeworx_feedback, and recent_changes, but the naming is largely predictable and clearly grouped by prefixes like polymarket_ and pipeworx_.

Tool Count2/5

With 33 tools, this server exceeds the threshold where the count becomes a burden rather than a benefit. The set covers many disparate domains—general data querying, Polymarket betting, plant taxonomy, npm auditing, AI visibility, memory, and subscriptions—so the surface feels over-scoped for a single server.

Completeness4/5

The core research/query workflow is well covered: discovery, lookup, grounded answers, deep research, entity resolution, comparison, validation, and change tracking are all present. Subscription and memory lifecycles are also complete; the main gaps are minor, such as no explicit tool for fetching a pipeworx:// citation URI directly.

Resources