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Glama

Imei

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

A4.9/5.0
Behavior5/5

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

The description adds behavioral details beyond annotations: scoping by identifier, persistence differences between authenticated users (persistent) and anonymous (24 hours). No contradictions with annotations (idempotentHint=true, etc.).

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 concise (4 sentences) and well-structured: starts with purpose, then usage guidelines, then behavioral details, all without redundancy.

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?

Given the tool's simplicity (2 string params, no output schema), the description covers purpose, usage, parameter semantics, and behavioral aspects comprehensively.

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% with descriptions, but the description adds value by providing concrete usage examples for key (e.g., 'subject_property') and value ('findings, addresses, notes'), enhancing 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 verb 'save' and the resource 'data' (key-value pair). It defines the tool's purpose as storing information for reuse, distinguishing it from siblings like 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?

The description explicitly says 'Use when you discover something worth carrying forward' and mentions pairing with recall and forget. This provides clear context and alternatives.

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

A4/5.0
Disambiguation3/5

Many tools are distinct in purpose, but there is significant overlap among the data-query tools: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, and discover_tools all route to the same underlying 5,756 tools. The descriptions help differentiate them, but an agent could easily select the wrong router for a given question.

Naming Consistency2/5

The naming is a mix of verb_noun (ask_pipeworx, resolve_entity, validate_claim), noun-only/product names (bet_research, compare_entities, entity_profile, deep_research), and generic terms (remember, recall, forget, subscribe, unsubscribe). Some tools have prefixes like polymarket_* and pipeworx_*, but ask_pipeworx variants deviate from the pattern. No consistent convention across the set.

Tool Count3/5

33 tools is on the heavy side but arguably justified by the broad domain (SEC data, FDA, prediction markets, subscriptions, memory, AI visibility, npm scanning, IMEI validation). However, several tools feel like they belong to separate concerns (IMEI check, npm dependency scanning, llms.txt generation), making the set feel unfocused.

Completeness4/5

The core Pipeworx query surface is well-covered: routing, grounded answers, deep research, entity profiles, comparisons, claim verification, and tool discovery. Subscription CRUD is complete (subscribe, recent_alerts, unsubscribe, list_subscriptions), and memory tools are complete (remember, recall, forget). Minor gaps: no direct tool for editing a stored memory, and IMEI tools are minimal (validate + check digit only).

Resources