Mycelium Emissions Database

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

With no annotations, the description carries the full burden of behavioral disclosure. It explains that figures include per-scope data, units, and whether values are reported by the company or estimated by Mycelium, giving meaningful insight into the response. It does not cover edge cases or error behavior, but for a read operation this is reasonably transparent.

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

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

The description is two sentences with no wasted words. The first sentence front-loads the action and key details, while the second gives a concise usage tip. Every clause adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity, no output schema, and empty annotations, the description sufficiently covers what the tool returns (scope breakdown, units, source tags) and how to customize the call via year. The required entity parameter is clearly documented. Nothing essential is missing for correct invocation.

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

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

Schema description coverage is 100%, so the baseline is 3. The tool description adds the 'Omit year' instruction, but this merely restates the schema's 'Defaults to the latest year on file.' The entity parameter is already well documented as coming from search-entities, so the description adds little beyond what structured data already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb 'Get' with a clear resource: a company's full greenhouse gas emission figures for one reporting year in the OpenCCF data model. It details what is included (per-scope lines, kgCO2e, reported/estimated tags), which clearly distinguishes it from siblings like get-entity-profile and search-entities.

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

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

The description clearly frames the intended use: retrieving emission figures for one reporting year, with the instruction to omit year for the latest reporting year. The schema adds guidance to obtain the entity slug from search-entities, implying a workflow. However, it does not explicitly contrast with sibling tools or state when not to use it.

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

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

With no annotations, the description carries the full burden of behavioral context. It states the profile is public, implies a read-only operation through 'Get', and clarifies the data scope including score ranges and emissions split. It does not mention failure modes or authentication needs, but for a simple public profile read this is reasonable disclosure.

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

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

The description is two sentences with no filler. It front-loads the core purpose, then lists what the profile contains, and ends with the actionable prerequisite. Every sentence contributes useful information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has only one required parameter and no output schema, but the description covers the major response fields and the prerequisite for obtaining the parameter. Some response shape details and error behavior are absent, but the description is sufficient for an agent to invoke the tool correctly.

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

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

The input schema already documents the single parameter fully, including that entity can be a slug or GUID and that the slug comes from search-entities. The description reinforces the slug workflow but adds little semantic value beyond the schema, so the high-coverage baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Get') and identifies the resource as a company's public Mycelium profile, then enumerates the key contents: identity/registration, Mycelium Score, industry rank, Transparency Score, and emissions summary. This makes its purpose clear and distinguishes it from the sibling tools without needing to inspect schemas.

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

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

The description explicitly tells the agent to use search-entities first to find the slug, which is a clear usage precondition. It does not explicitly contrast this tool with get-emission-figures or state when not to use it, but the context is strong enough for basic routing.

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

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

With no annotations at all, the description carries the full burden of behavioral disclosure. It discloses what the tool returns (matching companies with Mycelium Score and slug handle) and the optional country filter, which adds useful context. However, it does not describe matching semantics (fuzzy vs exact), result ordering, pagination limitations, or empty-result behavior, so there are clear gaps for a tool with no annotations and no output schema.

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

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

The description is compact and efficient: two sentences that front-load the action and scope, then state the return value and the optional filter. No irrelevant information is present, although it could have been slightly more expansive on usage context without becoming bloated.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple 3-parameter search tool with high schema coverage, the description covers the core purpose, the key return values, and the optional filter. The mention of the slug being 'used by the other tools' connects it to the sibling tools and provides useful workflow context. It lacks an explicit output schema and some behavioral detail, but overall the agent has enough to invoke it correctly.

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

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

Schema description coverage is 100%, so the schema already documents query, limit, and country with clear descriptions. The tool description adds no new parameter-level meaning beyond restating that search is by name and country is an optional filter. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb-resource pair: 'Search the Mycelium emissions database for companies by name.' It also distinguishes itself from the sibling getter tools by noting that it returns the slug handle used by the other tools, so an agent can recognize search-entities as the discovery step rather than a retrieval or detail tool.

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

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

The description implies usage: it is the tool that returns slugs for other tools, so it should be used first to identify entities. It does not explicitly state when not to use it or name alternatives like get-entity-profile or get-emission-figures, leaving some inference to the agent. This is adequate but not fully explicit.

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