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Glama

explain_method

Retrieve how the engine computes a topic (NPV, Greeks, curves) or how this server derives analytics, with cited docs, source links, and gaps.

Instructions

How the engine computes something, cited to its own docs and source at the pinned tag; or how this server derives its analytics (no engine call).

Args: topic: one of npv, fair-rate, greeks-bump-and-reprice, theta, curve-bootstrap, value-curves, settlement-and-cash-settlement, volatility-types, calendars-and-overrides, day-counters-and-compounding, schedules-and-stubs, error-codes (engine pages), or connector-analytics (what swap_dv01, key_rate_ladder, scenario, fair_rate and reprice_with compute on top of engine outputs, cited into this server's own source).

Returns the page as markdown (plain-language summary, the cited excerpts each with a path@tag:Lstart-Lend citation and a GitHub permalink, the request fields that control the behaviour, and what is NOT documented), plus citations, links (the permalinks), repository and not_documented as lists; repos gives the engine and connector repository URLs. Quote the citations (or hand over the links) when you explain a number; never assert a cause the page does not support.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.4

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose notable traits: engine pages make an engine-backed lookup while `connector-analytics` involves 'no engine call', returns include a `not_documented` list (transparency about limitations), and it surfaces provenance. It does not discuss cost, latency, or whether the output is cached, so a small gap remains for a documentation tool.

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?

Front-loaded with the purpose, then well-structured Args/Returns blocks with no filler sentences. The long comma-separated topic list and the closing citation-discipline sentence make it denser than ideal, but every part carries information.

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?

Even though an output schema exists, the description still names the returned keys (markdown, citations, links, repository, not_documented, repos), tells the agent what a page contains, and enumerates valid inputs. Nothing an agent needs to select or invoke this correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the schema has only `topic: string`, so the description compensates fully by enumerating every valid value (npv, fair-rate, greeks-bump-and-reprice, … error-codes, plus connector-analytics) and explaining what the analytics value means. This is meaning that exists nowhere in the 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?

States a specific verb (explain) and resource (how the engine computes a method / how this server derives its analytics), and explicitly separates the engine-page topics from the `connector-analytics` topic that describes what sibling tools like swap_dv01 and key_rate_ladder compute. An agent can distinguish this from price_* / build_* siblings without opening the schema.

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?

Gives clear context: use it for cited engine/connector documentation, and it explicitly routes the analytics question to the `connector-analytics` topic rather than an engine page. It also prescribes behaviour ('quote the citations or hand over the links when you explain a number; never assert a cause the page does not support'), though it does not give explicit when-not-to-use exclusions.

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