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Glama

Statec Lu

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

A4.3/5.0
Behavior4/5

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

Annotations provide idempotent=true and readOnly=false, and the description adds valuable context about persistence: 'scoped by your identifier', 'Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours'. No contradiction with annotations.

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 well-structured, front-loaded with the primary purpose, and every sentence adds relevant detail (use cases, scoping, TTL, companion tools). No fluff.

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?

For a simple two-parameter write tool with no output schema and strong annotations, the description provides enough context: when to use, persistence behavior, and relationship to recall/forget. No significant gaps.

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?

The input schema already fully describes both parameters with examples (100% coverage). The description only adds that it's a key-value pair, which is redundant with the schema, so baseline score of 3 is appropriate.

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's function: 'Save data the agent will need to reuse later' across conversations or sessions. It also distinguishes itself from sibling tools by explicitly pairing with recall and forget, making the purpose unambiguous.

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 gives concrete use cases ('Use when you discover something worth carrying forward') and names complementary tools (recall, forget). It doesn't explicitly state when not to use it, but the guidance is strong enough to select the tool appropriately.

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
Disambiguation3/5

The three ask_pipeworx variants (stable, beta, grounded) plus deep_research and validate_claim create real selection ambiguity — an agent could easily pick the wrong one. Many other tools (entity_profile, bet_research, scan_dependency) are clearly distinct, but the overlapping meta-query tools muddy the boundary.

Naming Consistency3/5

Naming is a mix of verb-initial (get_data, resolve_entity, generate_llms_txt, scan_dependency) and noun-initial (dataflow_structure, entity_profile, polymarket_edges, pipeworx_trending) conventions. The ask_pipeworx family and Polymarket cluster are internally consistent, but there is no single predictable pattern across the set.

Tool Count2/5

34 tools is heavy, and the server named 'Statec Lu' (Luxembourg statistics) carries 30+ tools for prediction markets, npm dependencies, AI visibility, memory, and subscriptions. It reads as an everything-server rather than a focused statistics integration; most tools have nothing to do with STATEC.

Completeness4/5

Within the STATEC domain, list_dataflows → dataflow_structure → get_data is a complete browse-and-query workflow. The broader domains also have good coverage (memory save/recall/forget, subscription list/create/cancel, rich Polymarket research tools). Minor gaps like no data-format conversion or direct 'latest value' shortcut exist, but they are workable.

Resources