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Glama

Fintech Intel

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. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "key": "user_research_topic"
+  },
+  {}
+]
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, and non-destructive traits. The description adds meaningful behavior beyond those: scoping to the user's identifier (IP, key hash, account ID) and the behavior of omitting the key to list all keys. It stops short of describing return shape, but adds value over 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?

Two sentences, front-loaded with the verb and resource, with no wasted words. Each clause contributes: retrieve/list behavior, use-case examples, scoping, and pairing with remember/forget.

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 single-optional-parameter tool with no output schema, the description covers purpose, usage context, scoping, and lifecycle pairing. The lack of return-format details is not a gap here because the operation is inherently simple and the context signals are rich.

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?

The schema already documents the 'key' parameter with 100% coverage. The description adds crucial semantics: omitting the key lists all keys, and the value was stored via 'remember,' clarifying what a key refers to. This exceeds the schema's baseline.

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 retrieves a value saved via 'remember' or lists all saved keys when the key is omitted. It names the specific source ('remember') and distinguishes from siblings ('forget' deletes), making the read operation unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives clear context: use it to look up previously stored context (ticker, address, research notes) without re-deriving it. It also references how it pairs with 'remember' and 'forget,' but does not explicitly state when not to use it or identify alternative tools for similar lookups.

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

A3.7/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical, discover_tools/suggest_questions/pipeworx_trending all serve discovery, and multiple Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research) find opportunities. Descriptions help but an agent could easily pick the wrong data-query tool.

Naming Consistency4/5

Tool names follow a consistent snake_case pattern, mostly verb_noun (ask_pipeworx, resolve_entity, validate_claim) and prefix groups (fintech_*, polymarket_*). Minor deviations like entity_profile or recent_changes are noun phrases but still readable and predictable.

Tool Count2/5

34 tools is excessive for a coherent set; the server appears to bundle a general-purpose data research platform, prediction-market analysis, memory, and subscriptions under one 'Fintech Intel' name. Many meta-tools could be split into separate servers, and the count burdens an agent with unnecessary choices.

Completeness4/5

The tool surface covers the fintech/data-intel domain thoroughly: SEC filings, FDIC, FDA, economic data, real estate, prediction markets, monitoring subscriptions, and memory. Gaps are minor—e.g., no direct tool for historical stock charts, but the router (ask_pipeworx) handles such queries.

Resources