Skip to main content
Glama

Api Ninjas

by io.github.pipeworx-io

Remember

remember
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?

Beyond annotations (idempotentHint=true, destructiveHint=false), description adds valuable context: scoped by identifier, persistent for authenticated users, 24-hour retention 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.

Conciseness4/5

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

Description is concise with four focused sentences. Front-loaded with purpose. Slightly dense but efficient; could benefit from more structuring but remains clear.

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?

With no output schema, description adequately explains persistence behavior, scoping, and pairing with sibling tools. Covers all necessary context for an agent to use the tool correctly.

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 descriptions for both key and value. Description adds value by explaining the key-value pair concept and giving example key formats, though schema already includes examples.

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?

Description clearly states the tool saves data for reuse across conversations or sessions, using a verb-resource pair ('Save data'). It distinguishes from siblings like recall and forget by mentioning them explicitly.

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?

Explicitly states when to use: 'when you discover something worth carrying forward'. Provides alternatives: 'Pair with recall to retrieve later, forget to delete.'

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

All Api Ninjas toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.1/5.0
Disambiguation3/5

Most tools have distinct roles, but there is meaningful overlap among the question-answering family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and among the Polymarket analysis tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk). The long descriptions help separate them, but the boundaries are still subtle enough that an agent could easily pick the wrong variant.

Naming Consistency4/5

The naming is mostly snake_case and generally follows a verb_noun pattern (ask_pipeworx, compare_entities, resolve_entity, scan_dependency, validate_claim). Deviations like entity_profile, recent_alerts, recent_changes, and bare verbs (forget, recall, remember, subscribe, unsubscribe) are minor and do not seriously harm predictability.

Tool Count3/5

31 tools is heavy for a single server and suggests the surface is a bundled platform (data queries, prediction markets, memory, subscriptions, AI-visibility checks) rather than one tightly scoped domain. Each tool has a rational purpose, but the sheer count plus several meta/didactic tools makes the set feel somewhat oversized.

Completeness4/5

Core workflows are well covered: entity resolution, profiles, comparisons, grounded lookup, fact-checking, deep research, memory CRUD, and subscription lifecycle. Gaps are minor. There are no update operations for subscriptions, and some optional data sources degrade softly, but agents can accomplish the intended research, monitoring, and memory tasks without dead ends.

Resources