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Glama

Hibp

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

A4.7/5.0
Behavior4/5

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

Annotations show readOnlyHint=false (write operation), idempotentHint=true (safe to retry), destructiveHint=false. The description adds scoping by identifier, persistence vs. 24-hour retention. No contradictions. Could mention that overwriting same key replaces value, but not critical.

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 (3 sentences) and front-loaded with the primary purpose. Every sentence adds value: purpose, usage guidance, behavioral details, and a mention of companion tools. No wasted words.

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?

Given no output schema, the description covers all necessary aspects: purpose, parameters, usage context, behavioral traits, and retention policy. It is complete for a simple key-value store tool.

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 both parameters described. The description adds meaningful context beyond schema: examples of keys (subject_property, target_ticker, user_preference) and values (findings, addresses, preferences, notes), aiding correct usage.

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 purpose: to save data for reuse across conversations/sessions. It provides concrete examples (resolved ticker, target address, user preference, research subject) and distinguishes itself from sibling tools recall and forget, which are explicitly mentioned.

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 explicitly states when to use the tool ('when you discover something worth carrying forward') and provides specific examples. It also guides the agent to pair with recall for retrieval and forget for deletion, giving clear alternatives.

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

A4.2/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, and the long, detailed descriptions make it easy for an agent to pick the right one. However, there is some functional overlap between research-oriented tools (e.g., ask_pipeworx vs deep_research vs bet_research) and between visibility-checking tools (ai_visibility_check vs scan_competitor_ai_presence), which could potentially confuse an agent without careful reading.

Naming Consistency4/5

Tool names are uniformly lowercase with underscores, and related tools share consistent prefixes (e.g., polymarket_*, ask_pipeworx, check_*). While most follow a verb_noun pattern, some are noun-first (entity_profile, polymarket_arbitrage) or compound (scan_competitor_ai_presence), creating minor inconsistency, but overall the naming is readable and not chaotic.

Tool Count4/5

With 37 tools, the count is on the higher end but appropriate for a broad data/research platform that covers HIBP breaches, multiple data-pull/research modes, Polymarket analytics, memory, subscriptions, and meta-tools. The number is justified by the diversity of capabilities, though it exceeds the typical 3-15 range for a narrowly-scoped server.

Completeness5/5

The tool set appears thorough for its intended scope: HIBP breach lookup is fully covered (check_account, check_password, get_breach, list_breaches, list_data_classes), research and entity resolution are extensive (entity_profile, compare_entities, deep_research, validate_claim, etc.), Polymarket has dedicated arbitrage/edge/fill-risk tools, and memory/subscription/meta capabilities are present. No obvious gaps for the stated functionality.

Resources