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Glama

Boe Uk

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

A4.5/5.0
Behavior4/5

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

The description reveals persistence behavior (authenticated persistent, anonymous 24 hours) and per-identifier scoping, which go beyond the provided annotations. Annotations already indicate idempotent and non-destructive; the description adds storage lifecycle context. Does not detail overwrite semantics or return values, but this is minor.

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 sentences: purpose, usage trigger with examples, and persistence/scoping/related tools. Every sentence carries useful information, front-loaded with the action.

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 two-parameter setter, the description covers what, when, where (scoping), and how long memory persists, along with companion tools. Absence of output schema makes return details unnecessary.

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. The description's examples (ticker, address, preference) loosely align with value semantics but add little beyond the schema's 'findings, addresses, preferences, notes'. 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 opens with 'Save data the agent will need to reuse later', a specific verb+resource, and clarifies scope across conversations/sessions. It explicitly names sibling tools recall and forget, distinguishing from them.

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?

The description provides an explicit 'Use when' trigger with concrete examples (ticker, address, preference, research subject) and states it's for avoiding re-lookup. It also names alternatives: recall for retrieval, forget for deletion.

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
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route the same 5,596 tools, with beta currently identical to stable. The suite of six polymarket_* tools and the ai_visibility_check / scan_competitor_ai_presence pair also risk misselection, despite detailed descriptions.

Naming Consistency4/5

The overwhelming majority of tools follow a clear snake_case verb_noun pattern (ask_pipeworx, resolve_entity, validate_claim, subscribe). Minor deviations exist for bare data endpoints like bank_rate, sonia, and eur_gbp, but these are still predictable and readable.

Tool Count2/5

37 tools is heavy for a single server, beyond the 25-tool threshold indicating a bloated surface. While the broad research scope justifies some size, many tools are variants or wrappers of the same underlying router, which pads the count and creates cognitive load.

Completeness4/5

For a research and data gateway, the surface covers lookups, grounded answers, deep research, claim verification, entity resolution, prediction-market analysis, BoE series, memory, and subscriptions. Minor gaps exist—such as no direct update for stored entities and BoE series limited to four convenience wrappers—but agents can work around these.

Resources