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Glama

Fara

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

A4.6/5.0
Behavior5/5

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

Adds details beyond annotations: scoped by identifier, persistent for authenticated users, 24h for anonymous, and pairing with recall/forget. No contradiction 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?

Three sentences, front-loaded with purpose, each sentence adds value: what, when, how, and pairing.

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?

No output schema, but description adequately covers usage context and persistence. Could mention if return value includes confirmation, but not essential for a write operation.

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%, so baseline is 3. Description adds examples for key naming (e.g., 'subject_property') but no additional format or constraints beyond 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?

Description clearly states the tool saves data for reuse, gives specific examples (resolved ticker, target address, user preference), and distinguishes from siblings recall and forget.

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 says when to use (discovering something worth carrying forward) and mentions alternatives (pair with recall/forget) for related operations.

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_beta is explicitly identical to ask_pipeworx, and the multiple Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research, polymarket_edge_tracker, polymarket_fill_risk) all surface betting opportunities with only subtle differences. The line between ask_pipeworx, ask_pipeworx_grounded, deep_research, and discover_tools is also fuzzy, making misselection likely.

Naming Consistency3/5

All tool names use snake_case and are readable, but naming conventions are mixed: many follow verb_noun (ask_pipeworx, discover_tools, list_foreign_principals), while others use noun-first or adjective_noun (entity_profile, polymarket_arbitrage, recent_alerts). Several tools share the 'pipeworx_' or 'polymarket_' prefix without that prefix meaning a consistent action type.

Tool Count2/5

34 tools is high for a single MCP server, and the scope spans unrelated domains (general data lookup, prediction-market analytics, FARA registrations, memory, subscriptions, AI-visibility monitoring). This feels like several servers merged rather than one cohesive set; many tools could be split into focused modules without losing functionality.

Completeness4/5

Core workflows are well covered: flexible data querying (ask_pipeworx, grounded, deep_research), entity resolution, FARA search/document retrieval, memory CRUD, subscription lifecycle, and claim validation. There are minor gaps such as no direct tool for single-source parameterized queries (everything routes through the universal router) and no account/profile management, but agents can complete the main advertised tasks.

Resources