Skip to main content
Glama

evidence-ledger

by com.saasdossier

get_register

Returns SaaSDossier's release register: dossier numbers, vendors, editions, and licensing information. The register is the released catalogue: four finished evidence records built only from vendor-published sources across a fixed 55-field, 10-domain framework, every field recorded in one of two states — Documented, or Question surfaced. Each entry carries its vendor, dossier number, edition, evidence date, two-state counts and commercial status: Licensed Editions are US$1,500 one-time; the OpenAI Public Edition is free and complete. Separately from that catalogue, the same response carries the commission capability: a buyer can commission a SaaSDossier for another named vendor that does not already have a released dossier, US$1,500 one-time, refunded in full if the vendor-published material cannot support a defensible SaaSDossier record. A commission is not a released dossier and carries no dossier number until it is released. The released catalogue remains the four records. Use this tool to answer what SaaSDossier has released, what each record costs, and how a vendor that has no released record yet can be commissioned.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries full behavioral burden and discloses the fixed 55-field/10-domain framework, two recording states, pricing, refund condition, and that commissions are not released dossiers. It does not describe the raw response shape, but that is less critical for a zero-parameter catalog tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded and logically organized, but it is verbose and repeats details such as 'US$1,500 one-time' and 'the released catalogue remains the four records.' It could be tightened without losing key 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?

For a parameterless tool with no output schema, this description is unusually complete: it specifies catalogue contents, per-field states, commercial tiers, commission behavior, and when no dossier number applies. It leaves little ambiguity about what the tool can answer.

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 tool has zero parameters, so the schema contributes no input semantics; the description instead clarifies what the returned register contains and how commissions are represented. This meets the baseline expectation for a parameterless tool.

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 opens with a specific verb and resource: 'Returns SaaSDossier's release register,' then enumerates dossier numbers, vendors, editions, and licensing. It closes with concrete questions the tool answers, making it clearly distinct from siblings like get_methodology or get_openai_ledger.

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?

The final sentence gives explicit use cases: checking released records, costs, and commissioning a vendor without a record. It does not explicitly name excluded cases or compare against sibling tools, but the usage context is unmistakable.

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

All evidence-ledger toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.4/5.0
Disambiguation4/5

The four tools mostly have clear boundaries: methodology, register, ledger, and free dossier are conceptually distinct. However, get_free_dossier and get_openai_ledger both point at the same public OpenAI record, and an agent must read carefully to know one returns a PDF/URL and the other field-level evidence.

Naming Consistency5/5

All tool names follow a consistent get_ pattern with clear object nouns: dossier, methodology, openai_ledger, register. There is no mixed casing, verb style variation, or vagueness across the set.

Tool Count5/5

Four tools is a well-scoped size for an informational evidence ledger service: methodology, the public ledger, the free dossier, and the release register. Each tool earns its place and the set does not feel thin or bloated.

Completeness4/5

The core workflow is covered: understand the framework, access the free public dossier, retrieve field-level evidence, and view releases and commission options. The main gap is that licensed dossiers are described in the register but cannot be fetched directly, and commissioning is reported but not executable through a tool.

Resources