Meelion MCP

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

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds substantial behavioral nuance: open vs Pro mode with different result limits (5 vs 10, capped at 200 or 10), seeMoreOnSite pointing to the comparer, detailUrl using MCP.public_base_url, and a disclaimer that data is indicative and may change. This goes well beyond the structured annotations and gives the agent a clear picture of what to expect.

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 longer than two sentences but is well-structured: main purpose, mode behavior, usage guidance, data disclaimer, and English keywords. Every section adds value, though the English keyword list could be trimmed for brevity. It is front-loaded with the core action and maintains clarity throughout.

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 no output schema, the description carries the responsibility of explaining behavior. It covers result limits, URL construction, data reliability, and provides detailed usage scenarios. For a read-only tool with four optional filters, this description is thoroughly complete, leaving little ambiguity for an agent.

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 description enriches parameter meaning by explaining how 'limit' behaves differently in open vs Pro modes (teto 200 or 10), and it lists the filter dimensions (tipo, distribuidor, prazo, limite) in the opening line. This extra context about parameter constraints and usage justifies a score above baseline.

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 opens with 'Busca as melhores oportunidades de renda fixa disponíveis na Meelion', a specific verb (busca) combined with a clear resource (melhores oportunidades de renda fixa). It also lists concrete examples like CDB, LCI, LCA, CRI, CRA, and debêntures, which clearly distinguishes it from sibling tools focused on indicators, projections, details, or quotes. The purpose is unmistakable.

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 states when to use the tool ('Use quando o usuário quiser encontrar onde o dinheiro pode render mais, comparar... ver rankings...'), providing clear use cases. However, it does not mention when not to use it or reference alternative sibling tools, so it falls short of the highest bar for explicit exclusions/alternatives.

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?

Annotations already declare read-only and non-destructive, so the safety profile is covered. The description adds that values are 'atualizados' and 'valores vigentes', clarifying data is current, but it doesn't describe the return format or update frequency, leaving some behavioral gaps.

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 moderately long but front-loaded with the main action, followed by usage guidance and English keywords. The English keyword section adds length but serves multilingual matching, so it earns its place.

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 no-parameter, read-only tool with no output schema, the description covers purpose, usage, and scope well. It lists exact indicators and differentiates from siblings, though it stops short of describing the response structure, which would improve completeness.

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 tool has no parameters and an empty schema, so there is nothing for the description to explain. Per the evaluation baseline for zero parameters, this scores 4.

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 it returns current Brazilian financial indicators (Selic, CDI/DI, IPCA, poupança) with a specific verb and resource. It distinguishes from get_future_projections by explicitly addressing expectations/future curve, avoiding confusion with siblings.

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?

It provides explicit usage scenarios: when users ask about current rates, economic indicators, inflation, interest rates, or fixed-income benchmarks. It also directs users to get_future_projections for market expectations or future curves, giving a clear alternative.

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?

Annotations already declare read-only and non-destructive behavior. The description adds useful context about the data source (Meelion) and points to the underlying API endpoint, as well as noting the aggregation by year. No contradictions 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.

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

The description is three sentences plus a keyword list, front-loaded with the core purpose, then parameter instruction, then use cases. It is dense but each sentence contributes meaningful guidance, with no wasteful filler.

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 description provides purpose, data source, aggregation level, and clear usage scenarios. While it doesn't detail the response fields, the phrase 'projeções futuras' and the mention of the API endpoint give a sufficient high-level understanding for an agent to decide whether to use the tool.

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 schema provides 100% coverage with descriptions for all parameters, including the alias relationships and the implicit requirement that one of 'indice' or 'index' be provided. The description simply restates this guidance without adding new semantic information.

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 the tool returns year-aggregated future projections for Selic, DI, IPCA, or dollar, referencing the Meelion API endpoint. It distinguishes itself from siblings by focusing on future projections and explicitly naming the data source.

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 lists when to use the tool (e.g., expectations for interest rates, future inflation, implied DI market, Copom projection, implied curve, macro scenario for fixed income) and includes English keywords. It does not mention exclusions or alternative tools, but the context is specific enough to guide selection.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, covering safety. The description adds valuable context by detailing the returned content (tipo, emissor, distribuidor, rentabilidade, prazo, vencimento, garantias, riscos), which is not present in annotations. No contradictions.

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 efficient: two main sentences plus a keyword list. It front-loads the core purpose, then usage, then helpful English keywords. No wasted words, and the structure is clean.

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?

Despite no output schema, the description enumerates the types of details returned, giving the agent a clear picture of the tool's output. Combined with usage guidance and sibling references, it is complete for a simple read-only detail tool with two optional parameters.

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 coverage is 100%, with both id and slug already described. The description adds a slight clarification that either 'ID ou slug' can be used for lookup, but this is marginal and not fully explicit about exclusivity. Baseline 3 is appropriate given the schema handles the heavy lifting.

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 the tool returns complete details of a specific investment by ID or slug, listing the types of information included. It distinguishes itself from sibling get_best_investments by mentioning it deepens an item from that list.

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 provides explicit context for when to use the tool: 'Use quando o usuário quiser entender melhor uma oportunidade específica... ou aprofundar um item retornado por get_best_investments.' It references an alternative tool by name, but does not explicitly state exclusions or 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds that quotes are 'atualizadas' and for assets tracked by Meelion, but does not disclose behavior like refresh rates, data source, or response format. This is a modest addition but not a comprehensive 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 three sentences, each serving a clear purpose: stating the function, providing usage guidance, and listing English keywords for matching. It is front-loaded with the main purpose and contains no redundant 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 is simple with one optional parameter, no output schema, clear annotations, and a concise description. The description provides enough context for selection and invocation, though it does not describe the return format, which is a minor gap given the tool's simplicity.

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 optional 'assets' parameter is already well-documented. The description adds natural language mapping (dollar, euro, etc.) but does not introduce new syntax or constraints beyond the schema, so the baseline score 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 clearly states the tool's function: returning updated quotes for assets and currencies tracked by Meelion, with specific examples like dollar, euro, gold, silver, and Bitcoin. This distinguishes it from sibling tools focused on investments, indicators, and projections.

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 says when to use the tool ('Use quando o usuário perguntar sobre câmbio, cotação atual, variação de moedas, metais ou criptoativos...'), providing clear context. It does not mention when-not to use it or name alternatives, so it misses the top score.

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