Open Source Intelligence MCP

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

With no annotations, the description carries the full burden. It transparently discloses the $0.50 cost, the default to today's UTC date, the 402 payment/retry flow, and the Authorization Bearer bypass. This is rich behavioral context beyond what structured fields provide.

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 front-loaded with the primary purpose in the first sentence. The payment details are separated into a short second paragraph, and every sentence earns its place without redundancy.

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 complete operational context: what it returns, when to use it, how payment works, error handling, default behavior, and auth bypass. An output schema exists, so not explaining return values is acceptable.

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 already covers all three parameters (100% coverage), so baseline is 3. The description adds meaningful context beyond the schema by explaining the 402 payment flow, the requirement to re-call with the same args plus payment_tx, and the Bearer key bypassing payment.

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 a specific verb+resource: 'Get the top 5 signals from today's brief as structured JSON.' It clearly distinguishes itself from the sibling daily_brief by positioning itself as a cheap sample, and explicitly says it returns no prose.

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 states exactly when to use this tool: as a cheap sample of the full daily_brief so an agent can decide whether to buy the full brief. It also names the alternative (daily_brief) and gives pricing context.

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 provided, the description carries the full burden and discloses important behavioral traits: payment requirements ($0.01/query after free allowance), handling of 402 errors (payment_tx challenge), and authentication bypass (Bearer fnet_ key). It also names the data sources (PyPI, npm, libraries.io). It does not explicitly state read-only status or potential side effects, but the disclosed traits go beyond typical structured metadata.

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 concise and front-loaded: the first sentence delivers the primary purpose and key attributes, followed by a short payment note. Every sentence contributes either to what the tool does or how to handle payment, without unnecessary fluff. The structure could be slightly improved with paragraph breaks, but it remains efficient.

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 complexity (payment flow, multiple data sources, comparison logic) and the presence of an output schema (not shown but noted), the description covers the essentials: what it compares, the ecosystem constraint, payment handling, and max package limit (though the limit is in the schema). It is slightly incomplete due to the ecosystem inconsistency and lack of mention of potential failure modes beyond 402, but overall it gives the agent enough 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?

Schema description coverage is 100%, so the baseline is 3. The description adds some value by explaining the payment flow for payment_tx and scoping of agent_id to the free-tier counter. However, it contradicts the schema by omitting 'cargo' from the ecosystem parameter, and it does not add significant meaning for the 'packages' parameter beyond the schema's description. Overall, it does not fully capitalize on the opportunity to enrich parameter understanding.

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 ('Compare') and resource ('open-source packages') with a defined scope (side-by-side in one ecosystem). It lists concrete comparison attributes and positions itself as the 'which of these should I pick?' tool, distinguishing it from siblings. However, it mentions only 'npm or PyPI' while the schema also supports 'cargo', causing a minor inconsistency.

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 phrase 'The "which of these should I pick?" tool' provides clear context for when to use this tool—deciding between packages—and the 'side by side in one ecosystem' constraint implies same-ecosystem comparisons. It does not explicitly name alternatives or when not to use it, but the usage context is sufficiently clear.

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, and it excels: it discloses the $5 paid nature, expiration at next midnight UTC, provenance attestation, 402 retry logic with payment_tx, and that an Authorization: Bearer fnet_ key bypasses payment. This gives the agent a thorough understanding of costs, auth, and failure handling.

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 dense but every sentence carries necessary information: first mentions the brief contents and sources, then provenance, then payment/expiration/retry flow. It is well-structured and front-loaded with the core purpose, avoiding fluff despite its length.

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?

A complex paid tool with multiple params and no annotations, but the description covers all essential operational aspects: contents, sources, payment, date, expiration, auth bypass, and retry mechanism. An output schema exists, so return-value details are not needed in the description, and the description still provides ample context for correct invocation and handling.

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% (baseline 3), and the description adds meaningful context beyond the schema by explicitly explaining that payment_tx is used when re‑calling after a 402 and that the same args must be reused. It also ties agent_id to a 'free-tier counter', reinforcing its purpose, though the schema already describes this.

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 'Get the curated daily open-source intelligence brief' and enumerates specific contents (top trending repos, notable dependency risks, deprecated packages, growth projects) from named sources (GitHub, PyPI, npm, libraries.io). This distinguishes it from siblings like trending_repos or dependency_risk, which cover only individual signals.

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 clear context for when to use the tool: it's a daily aggregate brief, defaulting to today UTC, with expiration at midnight UTC. It explains the payment flow and re‑call procedure on 402, but it does not explicitly state alternatives or exclusions relative to sibling tools, so it lacks a 5-level explicit when/when-not.

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 provided, the description carries the full transparency burden and does well: it discloses the paid nature ($0.02 per query), free daily allowance (25/day), exact 402 recovery flow (pay challenge, re-call with payment_tx), and auth bypass via Bearer key. It also lists external data sources (PyPI, npm, libraries.io), implying network access. This is substantial behavioral context beyond basic read/write.

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 organized into a main purpose paragraph and a payment details paragraph. Every sentence contributes (purpose, metrics, positioning, sources, cost, error handling, auth). It is not overly verbose, though the density of payment details makes it slightly heavy. Structure is logical and front-loaded.

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 output schema exists, return-value details are covered elsewhere. The description covers the tool's scope, sources, payment model, error recovery, and auth bypass, which is enough for an agent to invoke it correctly. It does not specify behavior for invalid inputs or edge cases, but for a moderately complex tool with rich schema and output schema, this is adequate.

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 all four parameters (package_name, ecosystem, agent_id, payment_tx). The description mentions payment_tx in the context of the 402 flow, which echoes the schema's description rather than adding new meaning. No additional parameter semantics are needed or provided, matching the 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 a specific verb+resource: 'Analyze dependency vulnerability/risk for a software package' and enumerates concrete outputs (maintenance status, last update, download trends, dependents, deprecation status, risk_score). The phrase 'The "should I add this dependency?" tool' clearly positions it for dependency evaluation, distinguishing it from siblings like license_check or project_health.

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 'should I add this dependency?' framing gives a clear decision-oriented use case, but the description does not explicitly state when not to use it or name alternatives. It provides payment and error-handling instructions, which are usage-related but not about tool selection. Overall, context is clear but exclusions are absent.

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 must carry the burden. It discloses the source (GitHub API) and cost (FREE), which is useful, but does not explicitly state that the operation is read-only, nor mention error behavior or rate limits. For a simple check tool, this is adequate but not rich.

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 a single, front-loaded sentence with two short appended fragments ('Source: GitHub API. FREE.'). Every word earns its place; no fluff or repetition.

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 tool with one required parameter and an output schema, the description covers the main purpose, output categories, data source, and cost. It does not discuss edge cases or limitations, but the output schema fills gaps regarding return values, making it sufficiently complete.

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%, so the baseline is 3. The description does not add extra meaning to the 'repo' parameter beyond what the schema already provides (owner/name format), and the other two parameters are clearly marked as unused. No compensation needed.

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 phrase 'Check open-source license compatibility for a GitHub repository' uses a specific verb and resource, and the detailed list of outputs (license type, permissions, restrictions, commercial-use eligibility, compatibility guidance) clearly distinguishes it from siblings like compare_packages or dependency_risk.

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 implies the tool is for assessing license compatibility of a GitHub repo, giving sufficient context for when to use it. It does not explicitly name alternatives or exclusions, but the niche purpose makes the intended usage obvious.

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?

No annotations are provided, so the description must disclose behavior. It states 'Returns' implying a read-only operation and 'FREE' indicating no cost, but it does not explicitly mention side effects, authentication, or rate limits. For a 0-parameter info tool, this is adequate but not comprehensive.

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 and front-loaded with the tool name and 'FREE'. The second sentence is a long enumeration of sister servers, which is relevant but could be considered slightly cluttered. Overall, it is concise and each piece contributes to the tool's purpose.

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 has 0 parameters, an output schema exists, and it serves as an informational endpoint, the description sufficiently covers its purpose and the types of information returned. It explains the two key outputs, though it does not discuss the output format or additional context.

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 has 0 parameters with 100% schema coverage, so the baseline is 4. The description does not need to explain parameters but adds context about the content of the returned information, such as the provenance attestation and sister servers.

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 how to attach a verifiable provenance attestation' and lists the sister data servers, providing a specific verb and resource. It is distinguishable from sibling tools that focus on package analysis (e.g., compare_packages, license_check) by being an informational tool about the FoundryNet Data Network.

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 when one needs information about provenance attestation or the sister data servers, but it does not explicitly state when to use this tool versus alternatives. No exclusions or alternative tool references are mentioned, leaving usage context only implied.

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?

No annotations are present, so the description carries the full burden. It discloses significant behavioral details: the paid nature ($0.01 per query after 25/day free), 402 handling with payment challenge and re-call with payment_tx, agent_id scoping, and auth bypass. This goes well beyond the schema and helps the agent handle edge cases appropriately.

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 well-structured: a purpose-leading first paragraph with metrics and a composite score, then a compact second paragraph on payment specifics. Every sentence provides necessary information without redundancy. It is efficient and front-loaded for quick agent comprehension.

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 complexity (paid, composite score, retry logic), the description is comprehensive. It explains the output's health_score composition (popularity + activity + maintenance + governance) and the payment/auth behavior. The output schema exists and likely covers return values, so the description appropriately focuses on high-level semantics and operational caveats.

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%, so baseline is 3. The description adds value by explaining the roles of agent_id (scopes allowance) and payment_tx (used on 402 re-call) within the payment flow, which enriches the schema's terse descriptions. It does not add detail about 'repo' beyond the schema's example, but the overall parameter context is strengthened.

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: 'Check open-source project health for a GitHub repository' and enumerates specific metrics (stars, forks, issues, commit frequency, etc.) plus a composite health score. The tagline 'is this project alive and worth depending on?' further clarifies its distinctive role among sibling tools that focus on specific aspects like license or dependency risk.

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 a clear context for use ('The "is this project alive and worth depending on?" tool'), implying it is for evaluating project viability. It does not explicitly mention alternatives or when not to use it, but the phrasing strongly signals its primary use case. A 5 would require explicit exclusions or alternative tool names.

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 provided, the description carries full behavioral disclosure. It reveals the data source (GitHub API), snapshot-vs-live access modes, per-query cost, free allowance, the exact 402 payment-challenge retry flow, and the bearer key bypass. This is exceptionally transparent and goes beyond what the schema could convey.

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 opens with a crisp purpose statement, then adds the essential operational nuances (snapshot vs live, cost, payment retry) in a small number of sentences. Every sentence earns its place; there is no filler or repetition.

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 complexity (5 optional parameters, an output schema, and payment quirks), the description covers the key contextual aspects: growth metrics, filtering behavior, data source, pricing, free allowance, auth bypass, and error recovery. The agent can safely select and 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 provides a description for every parameter (100% coverage), so the description does minimal additional parameter work. It reinforces that language/topic parameters enable live search and that payment_tx is for 402 retries, but period and agent_id remain adequately explained by the schema alone.

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 states a specific verb ('Find') and a clear resource ('trending GitHub repositories') with growth metrics, and further distinguishes filtered vs unfiltered behavior. It is clearly distinct from sibling tools like compare_packages or license_check.

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 gives clear context on when to use filtered vs unfiltered queries ('Unfiltered queries are served from the daily-aggregated snapshot; filtered ones hit GitHub search live') and details payment/retry triggers. However, it does not explicitly name alternative sibling tools, so the 'vs alternatives' guidance is inferred rather than stated.

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