Skip to main content
Glama

Statec Lu

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)

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.

All Statec Lu toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation2/5

Multiple tools have blurry boundaries: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical (beta is explicitly 'exactly' the stable version), and ai_visibility_check vs scan_competitor_ai_presence is a single-vs-batch duplicate. The six polymarket_* tools are differentiated by long descriptions, but their overlapping concerns (edges, arbitrage, fill risk, spread) would frequently misroute an agent, and discover_tools vs suggest_questions also compete.

Naming Consistency3/5

Names follow two coexisting conventions: verb_noun for actions (get_data, resolve_entity, validate_claim) and domain-prefixed families (polymarket_*, pipeworx_*, ask_pipeworx_*). Within each family the pattern is consistent, but mixing the two styles across the set, plus outliers like generate_llms_txt and bare verbs (remember, forget, recall), makes the overall scheme feel uneven though still readable.

Tool Count2/5

34 tools is well past the 'heavy' threshold and the count is not justified by the server's stated identity: a server named 'Statec Lu' (Luxembourg statistics) contains only 3 STATEC tools buried among general data-platform, prediction-market, AI-visibility, npm-scanning, and memory utilities. The sprawling, multi-domain surface would be more coherent split into separate servers.

Completeness3/5

The STATEC subset is complete (list_dataflows → dataflow_structure → get_data forms a full browse/fetch lifecycle), and the broader research surface covers entity resolution, grounded answers, comparison, claim verification, and subscription/alert/memory management. However, the overall domain is incoherent—a STATEC server missing nothing for statistics but carrying 31 unrelated tools—and there are notable gaps such as no tool to directly fetch a pipeworx:// citation URI and no execution side for the extensive Polymarket analysis tools.

Resources