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Statfin Fi

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

A4.4/5.0
Behavior4/5

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

The description goes beyond annotations by disclosing persistence behavior: key-value scoping by identifier, authenticated users get persistent memory, anonymous sessions retain 24 hours. It also notes the pairing with recall/forget. While annotations indicate idempotent and non-destructive, the description adds crucial lifetime 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 concise yet comprehensive, with each sentence adding value: purpose, usage, storage details, and companion tools. It is front-loaded with the main action and efficiently structured without redundancy.

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 key-value store with no output schema, the description covers essential aspects: what to store, when to use, persistence rules, and relationships to other tools. It could mention return value or error scenarios, but for this low-complexity tool, the existing information is sufficient.

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 covers both parameters with examples (key and value). The description does not add new parameter-specific semantics, but it contextualizes them through usage examples. With 100% schema coverage, this is a solid baseline; no additional parameter info is needed.

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' with a specific verb (save) and resource (data). It distinguishes itself from siblings by explicitly pairing with recall and forget, making its role in the memory system unambiguous.

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?

It provides explicit usage guidance: 'Use when you discover something worth carrying forward' with concrete examples (resolved ticker, target address, user preference, research subject). It also mentions the companion tools recall and forget, offering clear context for when to use this tool versus alternatives.

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

All Statfin Fi toolsInspect
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TDQS

A4/5.0
Disambiguation3/5

Several tool families overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded serve nearly the same routing purpose (beta explicitly 'currently matches ask_pipeworx exactly'), and the five polymarket_* tools plus bet_research create a dense cluster an agent must pick through. The descriptions are unusually detailed and do differentiate them, but the boundaries between the ask_pipeworx variants and between bet_research/polymarket_edges/arbitrage remain easy to misselect.

Naming Consistency4/5

All names are lowercase snake_case and mostly follow verb_noun or domain-prefix patterns (ask_pipeworx, polymarket_edges, list_subscriptions, resolve_entity). Minor deviations exist: subjects and table_meta are bare nouns rather than verbs, the ask_pipeworx family uses an ask_ prefix while the closely related deep_research does not, and entity appears as both a prefix (entity_profile) and a suffix (resolve_entity).

Tool Count2/5

34 tools is well above the 25-tool threshold for 'too many,' and the mismatch is sharpened by the server name 'Statfin Fi': only 3 of 34 tools (query_table, subjects, table_meta) actually relate to Statistics Finland, while the rest are a sprawling multi-domain platform covering prediction markets, AI visibility, npm packages, memory, and subscriptions. The count is appropriate for a general data platform but not for the apparent StatFin scope, making the surface feel bloated and unfocused.

Completeness4/5

The platform covers the full research lifecycle: discovery (discover_tools, suggest_questions), identifier resolution (resolve_entity), lookups (ask_pipeworx, entity_profile, compare_entities), verification (validate_claim, ask_pipeworx_grounded), monitoring (subscribe, recent_alerts, recent_changes), and memory (remember/recall/forget), with no obvious dead ends. Minor gaps exist — there is no keyword search across the StatFin catalog (browse-only via subjects), and one-off tools like generate_llms_txt and scan_dependency feel bolted on rather than part of a coherent domain.

Resources