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Glama

Oecd

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

A4.6/5.0
Behavior5/5

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

Discloses key-value scoping, persistence duration (24h for anonymous, permanent for authenticated), and idempotency beyond annotations. No contradictions.

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?

Concise paragraph with no filler; front-loaded with purpose, then usage, then behavior, then sibling pairing. Every sentence adds value.

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 purpose, usage, behavior, and lifecycle. No output schema but references retrieval with recall. Missing explicit mention of overwrite behavior, but idempotent hint implies it.

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 already covers both parameters thoroughly. Description adds examples of key naming conventions and clarifies value as free text, reinforcing 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?

Clearly states the tool saves data for reuse, with specific examples (resolved ticker, target address, etc.). Distinguishes 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 Guidelines4/5

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

Explicitly says when to use ('discover something worth carrying forward') and references companion tools (recall, forget). Could add what not to use it for, but adequate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Oecd toolsInspect
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TDQS

A3.9/5.0
Disambiguation2/5

Several near-duplicate lookup and prediction-market tools make selection ambiguous: ask_pipeworx_beta deliberately mirrors ask_pipeworx, and the five polymarket_* tools plus bet_research all target the same general 'should I bet / where is the edge' use case. The descriptions are detailed, but at the set level an agent must read extensive disambiguation essays to avoid picking the wrong tool.

Naming Consistency3/5

The set is uniformly snake_case, and subfamilies like ask_pipeworx*, polymarket_*, and subscribe/unsubscribe are internally consistent. However, conventions vary widely: verb_noun (fetch_dataset, validate_claim), noun phrases (entity_profile, bet_research), bare verbs (remember, recall, forget), and prefix-branded meta tools (pipeworx_feedback, pipeworx_trending) all coexist without a single predictable pattern.

Tool Count2/5

34 tools for a server nominally called 'Oecd' vastly exceeds the scope implied by the name and crosses the 25+ too-many threshold. Many tools belong to unrelated domains such as Polymarket arbitrage, npm dependency scanning, llms.txt generation, and AI visibility audits, making the set feel like a broad dumping ground rather than a focused tool server.

Completeness4/5

Within its sprawling domains the tool surface is fairly complete: lookup, grounded verification, deep research, entity resolution/profile/comparison, memory, subscriptions, alerts, and OECD dataflow search/list/fetch are all represented. There are minor gaps such as lack of direct OECD metadata descriptions or deeper navigation of the 5,708 underlying tools, but most workflows can be completed without dead ends.

Resources