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Alphavantage

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

A4.9/5.0
Behavior5/5

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

Description adds valuable context beyond annotations: it clarifies the write nature (consistent with readOnlyHint=false), idempotency is implied (overwriting same key is safe), and non-destructive. It reveals scoping and expiration behavior, which annotations do not cover. 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?

Four sentences, each carrying essential information: core function, usage trigger, storage mechanism, persistence rules, companion tools. No redundant words. The most critical info ('Save data') is first.

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 key-value store with no output schema, the description covers all necessary context: what it stores, when to use, scope, persistence, and related tools. No gaps given the tool's 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 coverage is 100% with descriptions for both parameters. The description adds example key formats ('subject_property', etc.) and clarifies value as free-text, which enhances meaning. Baseline 3 is elevated to 4 because the examples provide practical guidance beyond 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 clearly states the tool's function: 'Save data the agent will need to reuse later' with a specific verb and resource ('save key-value pair' is implied). It distinguishes clearly from siblings by naming 'recall' and 'forget' as companion tools, making purpose unambiguous.

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?

Explicit guidance on when to use: 'Use when you discover something worth carrying forward' and what kinds of data (resolved ticker, target address, etc.). It also explains scope ('scoped by your identifier') and persistence differences between authenticated and anonymous sessions, and pairs with recall/forget.

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

All Alphavantage toolsInspect
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TDQS

A3.9/5.0
Disambiguation3/5

Some tools are very closely related—ask_pipeworx and ask_pipeworx_beta are currently identical, and ask_pipeworx_grounded, validate_claim, and deep_research all answer factual questions with only subtle differences. The av_* and polymarket_* families are clearly separated, and descriptions are detailed, so most overlapping tools are still distinguishable.

Naming Consistency3/5

The set uses readable snake_case throughout and has useful prefixes like av_, polymarket_, and ask_pipeworx_, but there is no uniform verb_noun convention. Verbs, noun phrases, and bare verbs are mixed (forget, recall, remember vs ai_visibility_check, bet_research, recent_changes).

Tool Count2/5

37 tools is heavy, and the scope sprawls across financial data, prediction markets, memory, subscriptions, AI visibility, dependency scanning, and llms.txt generation. Several feel like unrelated utilities attached to one server, and ask_pipeworx_beta is a redundant variant that could be removed.

Completeness3/5

Coverage is strong for the broad data research workflow: data lookup, deep research, entity profiles, memory, subscriptions, and prediction-market analysis all have working lifecycles. However, within the Alphavantage cluster there are obvious gaps—no cash flow, no symbol search, and no intraday/weekly/monthly or broader endpoint coverage.

Resources