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Glama

Excuse

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?

The description adds behavioral context beyond annotations: memory is 'scoped by your identifier', authenticated users get persistent memory while anonymous sessions retain for 24 hours. These details explain persistence semantics, which annotations do not convey. No contradictions with annotations.

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 succinct—three sentences—and front-loaded with the primary action. Each sentence provides useful information: purpose, when to use, and key behavioral details, with no fluff.

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 tool with no output schema and good annotations, the description fully covers the purpose, usage, persistence, and relationship to sibling tools. It is complete enough for an agent to select and invoke it correctly.

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%, so the baseline is 3. The description gives examples of key/value content (ticker, address, preference) but these mirror the schema's examples without adding new parameter-level details. It does not enhance understanding beyond the schema.

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 function: 'Save data the agent will need to reuse later' with a specific verb and resource (key-value store). It distinguishes itself from siblings by explicitly mentioning companion tools recall and forget, making the purpose unambiguous.

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 concrete when-to-use guidance: 'Use when you discover something worth carrying forward... so you don't have to look it up again.' It names recall and forget as alternatives/replacements, giving clear context on how this tool fits into a workflow.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Excuse toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

The ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim) all route to the same underlying 5,708-tool catalog with subtle behavioral differences that are easy to misselect. Entity-focused tools (entity_profile, compare_entities, recent_changes, scan_competitor_ai_presence) also overlap heavily in the data they return, making boundaries fuzzy.

Naming Consistency3/5

All names use snake_case, but the word-order convention is mixed: verb-first (ask_pipeworx, compare_entities, discover_tools) coexists with noun-first (entity_profile, polymarket_arbitrage, recent_changes). The inconsistent reversal in excuse_generate versus generate_llms_txt further breaks the predictable pattern.

Tool Count2/5

At 32 tools the set is well past the well-scoped range, and several entries feel like padding: ask_pipeworx_beta duplicates ask_pipeworx, while the seven Polymarket tools and five company-intelligence tools could each be consolidated into fewer distinct capabilities. The broad scope justifies a large surface, but this count makes the server difficult to navigate.

Completeness3/5

The core data-lookup lifecycle (resolve, fetch, ground, validate, research, compare) is well covered, and memory/subscription features round it out. Obvious gaps include no direct tool to read a pipeworx:// citation URI (despite deep_research referencing one), and the excuse_generate tool is an isolated one-off with no supporting tools in its domain.

Resources