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Glama

Tomtom

by io.github.pipeworx-io

Remember

remember
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

A4.3/5.0
Behavior4/5

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

Annotations provide idempotentHint=true; description adds scoping by identifier and persistence details (24h for anonymous). No contradictions. Adds value beyond 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: purpose, use case, technical detail. Front-loaded with verb 'Save'. Every sentence adds value; no fluff.

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

Completeness3/5

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

Does not describe return value or confirmation of storage. Given no output schema, some indication of success would be helpful. Otherwise covers key aspects.

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%, but description adds examples of key naming conventions and value types, enhancing understanding 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?

Explicitly states 'Save data the agent will need to reuse later' with specific examples (ticker, address, preference). 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 Guidelines4/5

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

Clearly says 'Use when you discover something worth carrying forward' and mentions pairing with recall/forget. Does not explicitly state when not to use, but context is sufficient.

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

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta (currently identical behavior), and ask_pipeworx_grounded are variants of the same router, and the six polymarket_* tools plus bet_research all operate in the same prediction-market space. The extremely detailed descriptions help an agent differentiate, but misselection risk remains real.

Naming Consistency4/5

Snake_case is used consistently and most tools follow a verb_noun pattern (resolve_entity, validate_claim, discover_tools), with predictable polymarket_ and pipeworx_ family prefixes. Minor deviations exist — entity_profile and recent_alerts are noun/adjective phrases, generate_llms_txt embeds a file extension, and single-word verbs (remember, route, geocode) break the strict pattern — but overall naming is coherent.

Tool Count2/5

At 35 tools, the server exceeds the comfortable range and bundles many unrelated domains: data lookup, prediction markets, geocoding/navigation, memory, subscriptions, AI visibility, npm scanning, and llms.txt generation. While every tool has a distinct purpose, the surface is heavy and would benefit from splitting into focused servers.

Completeness4/5

Each major cluster has strong lifecycle coverage: data lookup (router, grounded mode, deep research, discovery), company research (resolve, profile, compare, changes), prediction markets (research, arb, edges, fill risk, cross-venue spread), memory (remember/recall/forget), and subscriptions (subscribe/list/unsubscribe/alerts). Minor gaps exist — no direct Polymarket order placement and no explicit tool for fetching pipeworx:// URIs (left to resources) — but agents can accomplish the stated purposes.

Resources