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Glama

Eodhd

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

A4.7/5.0
Behavior4/5

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

Description adds context beyond annotations: scoping by identifier, persistence differences for authenticated vs anonymous users, and 24-hour expiry. No contradiction with annotations (readOnlyHint=false, idempotentHint=true).

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?

Single well-organized paragraph: purpose, usage, storage details, pairing with siblings. No redundant sentences; every sentence adds value.

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 2 required params and no output schema, the description is fully complete: covers when, what, how stored, and relationship to siblings.

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 descriptive parameter descriptions. Description adds semantic guidance (key naming conventions like 'subject_property', 'target_ticker'), providing extra 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?

Clearly states the tool saves data for reuse across conversations/sessions, with specific examples (ticker, address, preference, research subject). Distinguishes from sibling tools recall and forget.

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 tells when to use (discover something worth carrying forward) and when to use alternatives (pair with recall/forget). Includes scoping and persistence details.

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

A3.5/5.0
Disambiguation2/5

Multiple tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route research questions; polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, and bet_research all hunt prediction-market edges; ai_visibility_check and scan_competitor_ai_presence do nearly the same job. The verbose descriptions help, but an agent would still struggle to pick the right tool in these clusters.

Naming Consistency2/5

Everything is snake_case, but there is no coherent pattern: some names are verb_noun (compare_entities, validate_claim), some are bare verbs (remember, forget, recall), some are prefixed by domain (eodhd_*, pipeworx_*, polymarket_*, ai_visibility_*), and the Eodhd prefix clashes with the Pipeworx family. The conventions feel inherited from multiple unrelated codebases rather than a unified design.

Tool Count2/5

33 tools is already heavy for most servers, but the real problem is that only 2 tools (eodhd_eod_prices, eodhd_fundamentals) relate to the server's stated purpose. The other 31 span Pipeworx research, Polymarket betting, memory, subscriptions, feedback, llms.txt generation, and npm dependency checking — a massively over-scoped grab bag for a server named 'Eodhd'.

Completeness2/5

For the nominal EODHD domain the surface is severely incomplete: no splits, dividends, options, exchanges list, or other standard EODHD endpoints — just prices and fundamentals. As a general research/data platform it is broad rather than deep, mixing a decent question-answering core with unrelated one-off utilities, so no single coherent lifecycle is fully covered.

Resources