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Glama

Interpro

by io.github.pipeworx-io

Recall

recall
Read-onlyIdempotent

Retrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous IP, BYO key hash, or account ID). Pair with remember to save, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNoMemory key to retrieve (omit to list all keys)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context beyond these: scoping to an identifier (anonymous IP, BYO key hash, or account ID) and the special behavior of omitting the key parameter to list all keys. 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 concise at three sentences, each earning its place: first states the primary action, second provides a use case, third covers scoping and companion tools. It is front-loaded with the action verb and avoids redundancy with structured fields.

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 tool with one optional parameter, the description is complete: it covers both invocation modes (retrieve a key, list all keys), gives concrete use examples, explains scoping, and mentions companion tools. No output schema is present, but the return type is implicit from 'Retrieve a value' and 'list all saved keys', which is sufficient for this complexity.

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 description coverage is 100%, so the parameter is fully documented in the input schema. The description adds real-world examples of key values (user's target ticker, address, prior research notes) and reinforces the 'omit to list all' behavior, which goes slightly beyond the schema.

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 uses a specific verb 'Retrieve' and clearly identifies the resource ('a value previously saved via remember') and the alternative action ('list all saved keys'). It distinguishes itself from sibling tools like remember, forget, and get_entry by explicitly referencing them.

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 states when to use the tool: 'Use to look up context the agent stored earlier... without re-deriving it from scratch.' It also names companion tools and their relationship ('Pair with remember to save, forget to delete'), giving clear usage context.

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

A3.6/5.0
Disambiguation2/5

Several tool clusters have fuzzy boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all overlap on 'find and query data,' while the five polymarket_* tools plus bet_research form a heavily overlapping prediction-market cluster. The three genuine InterPro tools are clear, but an agent would frequently struggle to pick the right meta-tool.

Naming Consistency3/5

Most tools follow a readable snake_case verb-first pattern like compare_entities, resolve_entity, and validate_claim. However, bare verbs (remember, forget, recall), product-prefixed nouns (pipeworx_feedback, pipeworx_trending), and variant suffixes (ask_pipeworx_beta, ask_pipeworx_grounded) break the pattern enough to feel inconsistent.

Tool Count2/5

34 tools is well above the typical well-scoped range, and many tools duplicate or partially overlap each other's functionality. The count is further inflated by unrelated domains—AI visibility, prediction markets, memory, subscriptions, package auditing—bundled into a server nominally named 'Interpro.'

Completeness2/5

The InterPro subset (search_entries, get_entry, entries_for_protein) is minimal and lacks obvious protein/proteome-level operations, while the rest of the server covers so many unrelated domains that no single domain has clear end-to-end coverage. The Pipeworx query side is broad, but the overall surface feels like several incomplete toolsets merged rather than one complete product.

Resources