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Glama

valem

by io.github.vlad-public-code

Get domain guidance

get_domain_guidance
Read-onlyIdempotent

Get vetted instructions for the hard modelling shapes this domain involves (progressive tax/fee charges, per-period schedules, classification, date math, group-by, …). Read the model description in ANY language, pick the matching topic ids, and call this BEFORE authoring the spec — then follow what it returns. Available topics:

  • amortization_schedule — A computed array / per-period schedule: an amortization table, time series, or per-period breakdown (one row per period).

  • group_by — Group-by / aggregation over an array: subtotals, counts, sums per category.

  • date_math — Date arithmetic: days/months/years between dates, elapsed duration, age from a birth date.

  • classification — Deriving a label / tier / band / risk level / status from data via thresholds.

  • currency_conversion — Currency / FX conversion of an amount by an exchange rate.

  • status_field — A status / state field with flags or labels derived from the current status.

  • rank_percentile — Rank / percentile / leaderboard / quartile over an array of values.

  • regulated_charge — An official published charge whose rates are set by an authority: a tax, duty, levy, tariff, fee, toll, excise, or customs charge.

  • percentage — Percentages / ratios: percent-of, markup, discount, tax-inclusive vs exclusive, share-as-percent.

  • unit_conversion — Unit conversion / dimensional consistency: cm-m, kg-lb, C-F, miles-km — one canonical unit per quantity.

  • compound_growth — Compound growth / interest: future value, compound interest, exponential growth (the ** operator).

  • weighted_average — Weighted sum / weighted average / score over an array of values and weights.

  • eligibility — A boolean eligibility / qualification verdict derived from several criteria (and/or of conditions).

  • proration — Prorating / allocating an amount across a count or period (per-unit share, partial-period).

  • editable_items — A user-edited list of items the user adds, edits and removes: line items, debts, expenses, passengers, participants — each with its own fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicsYesIds of the guidance topics that apply (usually 0-2).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicsNo
guidanceNoConcatenated instructions for the requested topics.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / topics / items / enum
      Previous value: -[
-  "amortization_schedule",
-  "group_by",
-  "date_math",
-  "classification",
-  "currency_conversion",
-  "status_field",
-  "rank_percentile",
-  "regulated_charge",
-  "percentage",
-  "unit_conversion",
-  "compound_growth",
-  "weighted_average",
-  "eligibility",
-  "proration"
-]New value: +[
+  "amortization_schedule",
+  "group_by",
+  "date_math",
+  "classification",
+  "currency_conversion",
+  "status_field",
+  "rank_percentile",
+  "regulated_charge",
+  "percentage",
+  "unit_conversion",
+  "compound_growth",
+  "weighted_average",
+  "eligibility",
+  "proration",
+  "editable_items"
+]
  2. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, and non-destructive. The description adds beyond these by disclosing that results are vetted, that they should be followed, and that the tool should be invoked before spec authoring. It also explains the required input preparation, which meaningfully shapes agent behavior. 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?

The opening sentence front-loads purpose and workflow, and the topic list is compactly formatted with one-line definitions. Every line adds selection-relevant information; there is no redundant wording or filler.

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 read-only, single-parameter tool with an output schema, the description provides everything needed: what the tool does, when to call it, how to choose the parameter values, and what to do with the output. It does not need to explain return values because the output schema covers that.

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?

The schema only provides the enum ids and says 'usually 0-2'. The description adds a plain-language definition for every topic id, turning bare identifiers into decision-ready guidance. It also tells the agent how to choose topics by reading model descriptions, far exceeding the baseline for schema-covered parameters.

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 and resource: 'Get vetted instructions for the hard modelling shapes this domain involves', then enumerates the exact topic ids it serves. The scope is clearly distinct from sibling get_* tools and mutation tools, so an agent can identify which tool to call 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 explicit timing guidance: call this BEFORE authoring the spec, read the model description in any language, pick matching topic ids, and follow the returned instructions. It does not explicitly name alternatives or state when not to use it, but the workflow context is clear and actionable.

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

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but get_audit explicitly subsumes get_history and explain, and get_state with paths overlaps get_field, creating minor selection ambiguity. The detailed descriptions help, but an agent could still reach for the wrong getter.

Naming Consistency4/5

Naming is overwhelmingly consistent: snake_case with verb_noun structure and coherent get_/create_/delete_ clusters. Minor deviations like bare verbs (mutate, explain, restore, snapshot) and eval instead of evaluate prevent a perfect score.

Tool Count3/5

27 tools is above the comfortable range and feels heavy, especially with several overlapping audit/state getters that could be consolidated. That said, the domain is broad enough that the count is defensible, so it is heavy but not chaotic.

Completeness4/5

The tool set covers the full model lifecycle well: create, validate, test, mutate, evolve, read, delete, plus snapshot/restore, audit, blobs, views, library, and expression evaluation. Minor gaps like explicit export/import or separate view-management tools are workable around.

Resources