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Glama

Coordinates

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

A4.4/5.0
Behavior4/5

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

Annotations already indicate idempotent and non-destructive. Description adds value by explaining scope (key-value per identifier) and retention (24h for anonymous, persistent for authenticated users). 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?

Five focused sentences, each adding value. Front-loaded with core action, then expands with usage examples, persistence details, and related tool references. 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 write tool with two parameters and no output schema, the description covers action, usage, persistence, and siblings. Minor omission like size limits not critical.

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%, baseline 3. Description provides concrete examples of key-value pairs (e.g., 'subject_property', 'target_ticker') that add meaning beyond schema descriptions.

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 saves data for reuse, with specific examples (ticker, address, preference). It distinguishes from siblings 'recall' and 'forget' by explicitly pairing with them.

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

Usage Guidelines4/5

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

Explicitly says 'Use when you discover something worth carrying forward' and gives context about persistence differences based on authentication. No explicit when-not-to-use, but clear enough.

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

Many tools have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded are nearly identical; bet_research and polymarket_edges both analyze Polymarket markets). Descriptions are detailed but the sheer number of similar tools creates ambiguity for agents.

Naming Consistency4/5

Most tools follow a verb_noun pattern (e.g., list_subscriptions, create_subscription), but a few use noun_verb (bet_research) or are standalone nouns (centroid, midpoint). Overall, the pattern is fairly consistent despite minor deviations.

Tool Count3/5

35 tools is on the high side for an MCP server. The scope is broad (data access, prediction markets, geospatial, memory, subscriptions), so each tool earns its place, but the number is borderline for coherence.

Completeness4/5

The tool set covers a wide range of functionalities: data querying, entity profiles, comparisons, prediction market analysis, geospatial, memory, subscriptions, etc. Minor gaps exist (no batch operations or data export), but the core workflows are well-supported.

Resources