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Glama

Brapi

by io.github.pipeworx-io

Remember

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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

A4.2/5.0
Behavior4/5

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

Annotations include idempotentHint: true and readOnlyHint: false. The description adds meaningful context: 'Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours' and 'scoped by your identifier.' No contradiction with annotations; it enriches them with persistence and scoping details.

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 three sentences and highly efficient: it front-loads the purpose, gives usage examples, explains storage behavior, and references companion tools. Every sentence earns its place with 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 2-parameter tool with no output schema, the description covers purpose, usage, persistence, and related tools. It lacks explicit overwrite behavior, but idempotentHint: true covers that. Overall, it is sufficiently complete for the tool's simplicity.

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 description coverage is 100%, so baseline is 3. The description adds generic context like 'key-value pair scoped by your identifier' but does not provide additional parameter-specific semantics beyond what the schema already documents with examples.

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 states the tool's purpose clearly: 'Save data the agent will need to reuse later.' It identifies a specific verb and resource (save data) and distinguishes from sibling tools recall and forget by explicitly mentioning them as companions.

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?

The description provides clear usage context: 'Use when you discover something worth carrying forward' with concrete examples. It also names alternatives via 'Pair with recall to retrieve later, forget to delete,' though it does not explicitly say when not to use the tool.

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
Disambiguation3/5

Most tools fall into recognizable families (data lookup, entity research, prediction markets, memory, subscriptions), and the detailed descriptions help separate them. However, ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical variants, and several polymarket scanning tools overlap in purpose enough to cause misselection.

Naming Consistency4/5

Nearly all tool names are snake_case and readable, and families share clear prefixes like ask_pipeworx_*, polymarket_*, and pipeworx_*. The main inconsistency is that the Brazilian data endpoints use bare nouns (quote, crypto, currency, inflation, prime_rate) while most other tools use verb-like action names, so there is no single verb_noun pattern throughout.

Tool Count2/5

38 tools is well above the 25+ threshold for a heavy MCP surface, even though the server aggregates several distinct domains. Each tool may have a purpose, but the sheer count makes the set difficult to navigate and suggests the server is trying to be a platform rather than a focused toolset.

Completeness4/5

The server covers the full lifecycle for its core areas: lookup (ask_pipeworx, grounded, deep_research), entity workflows (resolve, profile, compare, recent_changes), memory (remember/recall/forget), and subscriptions (subscribe/list/unsubscribe/recent_alerts). Minor gaps exist, such as no subscription update/pause and no direct tool to fetch an arbitrary pipeworx:// citation, but agents can work around these.

Resources