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Apis Guru

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

A4.5/5.0
Behavior5/5

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

Annotations indicate idempotentHint=true and destructiveHint=false. The description adds significant behavioral context beyond annotations: it discloses scoping by identifier, persistence for authenticated users vs 24-hour retention for anonymous sessions. There is 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured: it opens with the main action, then usage guidance, then behavioral details. It is a medium-length paragraph with no wasted words. Slightly verbose but all sentences add value.

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?

The tool has no output schema, but the description covers the essential aspects: what it does, how data is stored, scoping, and persistence. It does not mention error handling or return values, but for a simple memory store, the description is adequate.

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?

Schema coverage is 100% with clear parameter descriptions. The description does not add new parameter-level information beyond the schema examples. It provides context about what kind of values to store but not syntax or format details. Baseline is 3, and the description adds marginal value.

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: 'Save data the agent will need to reuse later.' It specifies it is for storing key-value pairs that persist across sessions or within a conversation. It distinguishes itself from sibling tools like recall and forget by explicitly mentioning pairing with 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 provides clear usage guidance: 'Use when you discover something worth carrying forward...so you don't have to look it up again.' It also tells how to pair with recall and forget, offering explicit context of when to use and how it relates to alternative tools.

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

A4/5.0
Disambiguation3/5

Several tools have overlapping purposes—ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-twins (beta currently matches the stable router exactly), and deep_research, entity_profile, recent_changes, and compare_entities all fan out across similar data sources. The long descriptions do help differentiate them, but an agent selecting quickly could easily pick the wrong variant.

Naming Consistency3/5

Most tools use snake_case, but the verb style is inconsistent: get_api, list_providers, and validate_claim use verb_noun, while remember/forget/recall are bare verbs and polymarket_arbitrage, entity_profile, and bet_research are noun phrases. The pattern is readable but not predictable enough to infer behavior from the name alone.

Tool Count2/5

At 35 tools, the surface is heavy, and many are hyper-specialized (five separate Polymarket tools, three ask_pipeworx variants, three memory tools). The breadth is defensible for a multi-domain data platform, but it goes past the comfortable 16-25 range and would benefit from consolidation.

Completeness4/5

The tool set covers the main research lifecycle well: discovery, routing, grounded answers, entity resolution, profiling, comparison, claim validation, change tracking, subscription management, and memory. Minor gaps exist—such as no explicit fetch-by-citation-URI tool and soft-failed patent coverage—but agents can generally work around them.

Resources