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guif

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. Added

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already indicate mutability, idempotency, and non-destructiveness. The description adds meaningful context: key-value pairs scoped by identifier, persistence for authenticated users, and 24-hour retention for anonymous sessions. This goes beyond annotations without contradicting them.

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?

The description is front-loaded with the primary purpose and uses several sentences, but each sentence adds distinct value (use cases, persistence behavior, companion tools). It could be slightly tightened, but there is no fluff.

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

Completeness4/5

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

For a simple two-parameter tool with no output schema, the description covers purpose, usage context, persistence semantics, and related tools. It is complete enough for an agent to select and invoke correctly, though it omits explicit overwrite behavior, which is partially covered by the idempotent hint.

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% and includes example keys and values. The description only reinforces the key-value pairing and adds the 'scoped by identifier' detail, but it does not significantly enhance parameter semantics beyond what the schema already 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 opens with a specific verb and resource: 'Save data the agent will need to reuse later.' It clearly states the tool's role as the write counterpart to recall and forget, and includes concrete examples of what to store, distinguishing it from sibling tools.

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?

It provides explicit when-to-use guidance with examples ('a resolved ticker, a target address, a user preference, a research subject') and explicitly mentions paired tools recall and forget for retrieve/delete, guiding the agent on the full 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

A4.3/5.0
Disambiguation5/5

Every tool has a clearly distinct purpose. The ask_pipeworx family is differentiated by grounded mode and beta status; prediction-market tools each target a specific analysis (arbitrage, edges, fill risk, cross-venue spread); species tools split search vs. detail vs. occurrences; memory and subscription tools are unambiguous. No two tools appear to do the same thing.

Naming Consistency5/5

Tool names follow consistent snake_case patterns grouped by domain: ask_pipeworx variants, polymarket_* tools, species tools (get_species, search_species, get_occurrences, occurrences_near), memory verbs (remember, recall, forget), subscription verbs (subscribe, unsubscribe, list_subscriptions), and descriptive nouns like entity_profile and deep_research. The style is uniform and predictable.

Tool Count3/5

At 35 tools, the count exceeds the 16-25 range that feels heavy, though it sits below the 50+ extreme. The server is a multi-domain data gateway covering entity research, prediction markets, species, AI visibility, and subscriptions, so the breadth is justified, but the sheer number borders on overwhelming and pushes the score down.

Completeness5/5

The tool surface covers the core CRUD lifecycle for each subdomain: subscriptions have create/list/delete and alert retrieval, memory has save/retrieve/delete, species has search/detail/occurrence lookup, and data queries offer multiple modes (universal, grounded, deep research, claim validation). No obvious dead ends—each workflow has the necessary follow-up tools (e.g., resolve_entity before lookups, search_within for large records).

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