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Glama

Inegi

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?

Adds context beyond annotations: describes scoping by identifier, retention rules for authenticated vs. anonymous users, and pairs with recall/forget. Annotations already indicate idempotency and non-destructiveness, so this is complementary.

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?

Very concise: three sentences that efficiently convey purpose, usage guidance, and behavioral details. No superfluous words.

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?

Covers all necessary aspects for a simple key-value store: what it does, when to use, scoping, retention. Might lack explicit overwrite behavior, but idempotent hint and examples make it clear.

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 clear descriptions. The tool description adds value by giving examples of keys and values, and explaining the memory scope, which enriches 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 saves data for later reuse, using a key-value store. It distinguishes itself from sibling tools like 'recall' and 'forget' by explicitly mentioning them as complementary actions.

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?

Provides specific use cases (resolved ticker, target address, etc.) and context for when to use the tool. Does not explicitly state when not to use, but the examples give strong guidance.

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

A4.2/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but there are some close groups: the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) require careful reading to distinguish subtle differences.

Naming Consistency3/5

Naming is inconsistent: some tools start with verbs (ask, compare, generate), others with nouns (inegi_indicator, entity_profile), and there is no uniform verb_noun pattern. However, within families, naming is consistent (e.g., ask_pipeworx*).

Tool Count4/5

With 33 tools, the server is on the higher side but not excessive. Each tool serves a distinct purpose, though some could be merged (e.g., ask_pipeworx variants). The count is justified by the breadth of domains covered (INEGI, Pipeworx, Polymarket, utilities).

Completeness4/5

The toolset covers a wide range of functionalities: Mexican demographic/economic data, general structured data queries, prediction market analysis, and utility tools. Minor gaps exist (e.g., limited direct API for some Mexican indicators), but overall the surface is comprehensive for the stated purpose.

Resources