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Glama

Openaire

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

A4.4/5.0
Behavior4/5

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

Annotations indicate idempotent, non-destructive write. Description adds scoping by identifier and retention details (persistent vs 24h). No contradictions. Missing mention of overwrite behavior, but idempotentHint covers it.

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?

Four sentences with front-loaded purpose, no fluff. 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?

With 2 required params, 100% schema coverage, and clear annotations, the description is nearly complete. No output schema is acceptable for a write tool. Slight gap: no mention of return value or confirmation.

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 describes both parameters with 100% coverage. Description adds naming conventions (e.g., 'subject_property') but doesn't add significant new meaning 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?

Description uses specific verb 'save' and resource 'data' with clear examples (ticker, address, preference). Distinguishes from sibling tools `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?

Explicitly states when to use ('discover something worth carrying forward') and provides pairing instructions with recall and forget. Also describes behavior for authenticated vs anonymous users.

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
Disambiguation2/5

Several tools have heavily overlapping or explicitly duplicate purposes: ask_pipeworx_beta is described as currently identical to ask_pipeworx, while ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim all route natural-language queries to similar lookup pipelines. The Polymarket tools also blur together (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk), and scan_competitor_ai_presence is just a wrapper around ai_visibility_check.

Naming Consistency3/5

There are readable verb-led names like search_publications, get_project, resolve_entity, and validate_claim, but the set mixes conventions with noun-phrase names like entity_profile, recent_alerts, polymarket_edges, and pipeworx_trending. The lack of a single verb_noun pattern makes the surface feel inconsistent, though each family is internally recognizable.

Tool Count2/5

At 37 tools, this is well beyond the 25+ threshold where an agent starts paying significant selection and context cost. The broad domain could justify some breadth, but many tools are meta-wrappers or near-duplicates (ask_pipeworx_beta, compare_entities, entity_profile, deep_research) that inflate the count.

Completeness4/5

Within its main subdomains, the surface is fairly complete: OpenAIRE search has matching get_project/get_research_product retrieval, memory has remember/recall/forget, subscriptions have subscribe/unsubscribe/list/recent_alerts, and there are discovery/onboarding helpers like suggest_questions and discover_tools. Minor gaps exist (e.g., no direct generic Polymarket market quote tool, no memory update besides overwrite), but no major workflow is a dead end.

Resources