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Hilma

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

A4.8/5.0
Behavior5/5

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

Annotations indicate idempotentHint=true and destructiveHint=false, and the description adds essential context: key-value storage scoped by agent identifier, with explicit lifetime details (persistent for authenticated, 24 hours for anonymous). 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?

The description is concise (~100 words), well-structured with a logical flow: purpose, usage, behavioral details, sibling pairing. Every sentence adds value with no 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 write tool with no output schema, the description adequately covers purpose, usage, and behavioral constraints. It could optionally mention overwrite behavior, but the idempotent hint and 'save' wording make it clear. Slightly incomplete regarding return or error handling, but acceptable.

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 coverage is 100% with key and value descriptions already well-defined. The description enhances meaning by tying parameters to use-case examples (e.g., 'resolved ticker' for key, 'findings' for value), helping the agent choose appropriate content.

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 starts with a clear verb+resource: 'Save data the agent will need to reuse later.' It explicitly distinguishes from sibling tools 'recall' and 'forget' by stating they are paired for retrieval and deletion.

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?

The description specifies when to use: 'Use when you discover something worth carrying forward...' and provides concrete examples (resolved ticker, target address, user preference, research subject). It also clarifies scoping and persistence differences for authenticated vs. anonymous sessions.

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

Several natural-language query tools overlap heavily: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, deep_research, and validate_claim all route questions to the same underlying Pipeworx catalog. The descriptions do clarify the differences eventually, but the boundaries are subtle enough that an agent could easily pick the wrong one, and discover_tools/suggest_questions also serve a similar onboarding role.

Naming Consistency3/5

Almost all names are snake_case and readable, but they mix verb-first names (ask_, compare_, discover_, validate_) with noun-first names (entity_profile, recent_changes, polymarket_arbitrage, pipeworx_trending). The domain prefixes like hilma_, polymarket_, and pipeworx_ help navigation, but there is no single predictable verb_noun convention across the set.

Tool Count2/5

At 34 tools, the surface is too large for the amount of genuine functional diversity. Several tools are near-duplicates (ask_pipeworx_beta vs ask_pipeworx, ai_visibility_check vs scan_competitor_ai_presence, suggest_questions vs discover_tools), and the set would be noticeably tighter around 20-25 tools without losing coverage.

Completeness4/5

The major workflow clusters are well covered: research/query modes, entity resolution and comparison, prediction-market analysis, memory, and subscription lifecycle management all have their key operations present. Minor gaps exist — such as Hilma notices returning only index metadata rather than full notice text — but there are no critical dead ends for the server's apparent multi-domain research purpose.

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