Social Trends Intelligence MCP

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

With no annotations provided, the description carries the full burden. It discloses payment model, daily free allowance, 402 error handling, re-call procedure with 'payment_tx', and authentication via 'Authorization: Bearer fnet_'. This is substantial behavioral transparency beyond basic read/write nature, though it omits potential rate limits or data retention policies.

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 front-loaded with the core purpose, then adds critical payment and error-handling details. Every sentence serves a purpose, though the pricing/integration section is dense. It could be slightly more streamlined, but for a paid tool, the detail is appropriate.

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 complexity (paid API, error recovery, authentication) and the existence of an output schema, the description covers all necessary context: what it does, sources, pricing, free allowance, error handling, and re-call procedure. It is complete enough for an agent to use correctly without additional information.

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 adds context for the 'brand' parameter (the core entity) and the 'payment_tx' parameter (tied to 402 handling), but these are already fairly described in the schema. The description does not significantly enhance understanding beyond what the schema 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 explicitly states 'Track brand mentions across Reddit and Hacker News with sentiment + context', providing a specific verb and resource, and clearly distinguishes the tool's scope from siblings like 'community_pulse' or 'topic_sentiment'.

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 when to use (tracking brand mentions) but does not provide explicit when-to-use or when-not-to-use guidance, nor does it compare directly with sibling tools. The context of paid usage and daily free allowance is included, but no alternatives or exclusions are mentioned.

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?

Discloses all relevant behaviors: returns structured JSON, no prose, defaults to today UTC, costs $0.50, handles 402 with payment_tx, and notes that a Bearer fnet_ key bypasses payment. With no annotations, this fully informs the agent.

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 well-structured: it starts with the core action, then explains use case, payment, default behavior, error handling, and authentication. Every sentence 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 complexity (payment, auth, error handling) and the existence of an output schema, the description covers all necessary contextual information for an agent to correctly invoke and handle 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?

Schema coverage is 100%, so baseline is 3. The description adds context beyond schema: 'date' defaults to today UTC, 'agent_id' scopes free-tier counter, 'payment_tx' is used when re-calling after a 402. This enhances 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 the tool's purpose: 'Get the top 5 signals from today's brief as structured JSON'. It specifies it's a cheap sample of the full daily_brief, distinguishing it from the sibling tool daily_brief.

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?

Provides explicit guidance: use when you want a cheap sample to decide whether to buy the full brief. Mentions alternatives (daily_brief). Also explains the payment flow and how to handle 402 errors, including the payment_tx parameter.

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, so description fully handles transparency. Discloses cost ($0.01/query after 25 free), authentication bypass, and 402 retry protocol. Does not mention other error behaviors or rate limits beyond payment.

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?

Two paragraphs: purpose then payment. Efficient but could be more structured with bullet points for payment steps. No wasted sentences.

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?

Covers all key aspects: purpose, usage, payment, error handling. Output schema exists, so return values not needed. Missing potential rate limit warnings or platform-specific quirks.

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%, baseline 3. Description adds practical context: agent_id scopes free counter, subreddit excludes 'r/', payment_tx used for retries. This enhances schema descriptions.

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?

Description clearly states the tool checks community pulse on Reddit or Hacker News, specifying outputs like hot topics, sentiment, and activity level. It differentiates from siblings by focusing on multi-platform community 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?

Provides guidance on targeting subreddits or HN topics, and details payment handling including free tier limits and 402 error recovery. However, no explicit comparison to sibling tools for selective use.

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 fully discloses behavioral traits: it is a paid tool ($5), defaults to today UTC, expires at midnight, includes a provenance attestation, and provides a detailed 402 flow for payment recovery. 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 moderately long but well-structured. It front-loads the main purpose in the first sentence, then provides payment details and provenance in separate sentences. Every sentence contributes essential information, but some redundancy could be trimmed (e.g., 'each brief carries...' could be integrated).

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 100% schema coverage and existence of an output schema, the description provides a complete picture: it details the output contents (trending topics, viral content, sentiment shifts, communities, provenance attestation) and handles edge cases (payment failure, expiration). No gaps.

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 4 parameters. The description adds meaning beyond the schema by explaining the payment flow for payment_tx and stripe_token (e.g., 'when re-calling after a 402'), the scoping of free-tier counter for agent_id, and the default/expiration behavior for date. Thus adds significant value.

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 verb 'Get' and the resource 'curated daily social-trends brief', specifying the sources (Reddit, Hacker News, Google Trends) and content (trending topics, viral content, sentiment shifts, communities). This distinguishes it from sibling tools like trending_topics or brand_mentions.

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 (to get the daily brief), the default behavior (today UTC, expiration at midnight), and the payment process (402 handling, payment_tx, stripe_token). It lacks explicit guidance on when not to use it or comparison with alternatives, but the context is 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 transparently states the tool is free and returns informational data. It lists exactly what is returned, though it does not cover potential limitations or access requirements, which are minimal given zero parameters.

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 only two sentences, with no wasted words. It front-loads the core purpose and immediately follows with useful details, making it highly 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 has no parameters and an output schema (not provided but flagged), the description fully covers what the agent can expect: attestation guidance and a list of related data servers. No gaps remain for effective use.

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 schema coverage is 100% (trivially). The description adds context about the return value (attestation details and server list), which is meaningful beyond the empty schema.

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 purpose: 'Get FoundryNet Data Network info + attestation details.' It specifies what it returns (attestation instructions and a list of sister data servers), which is distinct from sibling tools that focus on specific analyses like brand mentions or sentiment.

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 does not explicitly state when to use this tool versus alternatives. While the list of sister servers hints at its role as a discovery endpoint, there is no direct guidance on prerequisites or scenarios for invocation.

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, description fully carries the burden. It discloses deterministic lexicon heuristic (no LLM), payment costs, error handling (402), and scope of free-tier counter via agent_id. 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?

Description is concise with two clear paragraphs: first for functionality, second for payment/error handling. No wasted words, front-loaded with core 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 output schema exists, description covers all essential aspects: platforms analyzed, output components (score, breakdown, trend, threads), payment model, and error recovery. Complete for effective use.

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%, but description adds value by explaining agent_id scopes the free-tier counter and payment_tx is for re-calling after a 402. This context is beyond the schema descriptions.

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?

Description clearly states it analyzes cross-platform sentiment for a topic on Reddit and Hacker News, providing overall score, per-platform breakdown, volume trend, and key threads. This distinguishes it from sibling tools like brand_mentions or trending_topics.

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?

Description explains payment model, daily free allowance, and 402 handling with re-call instructions. It also mentions an optional Authorization header to bypass payment. However, it does not explicitly state when to use this tool versus 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?

With no annotations, the description fully bears the transparency burden. It discloses payment model, daily free quota, error handling for 402, and auth bypass. It does not explicitly state read-only nature, but the behavior is largely 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 concise: two short paragraphs with the first explaining functionality and the second covering payment/auth. It is front-loaded with key purpose and contains no unnecessary content.

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 presence of an output schema, return values need not be explained. The description covers purpose, payment flow, and auth. It lacks sibling differentiation but is otherwise complete for a tool with good schema coverage.

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 decent parameter descriptions. The tool description adds context about agent_id for scoping free-tier counter, but does not add substantial meaning beyond what the schema provides. Baseline 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 tracks trending topics across social media with velocity scores, listing specific sources (Reddit, Hacker News, Google Trends). It distinguishes from siblings like brand_mentions or topic_sentiment by focusing on real-time trending across multiple platforms.

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 detailed payment and error handling instructions, including free allowance, 402 response handling, and auth bypass. However, it lacks explicit guidance on when to use this tool vs alternatives like brand_mentions or community_pulse.

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 fully discloses behavioral traits: paid nature, free allowance, 402 handling, Authorization bypass, and ranking metric (velocity per hour).

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?

Two paragraphs, front-loaded with core purpose. The pricing paragraph is necessary but slightly lengthy; could be tightened without losing essential info.

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, description need not explain returns. It covers pricing, error handling, and general functionality, fully adequate for a tool with 4 params and clear siblings.

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 only context for payment_tx (error handling), but does not enhance meaning of other parameters beyond schema.

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 tracks viral content on Reddit and Hacker News, ranked by velocity per hour, using the phrase 'what's about to blow up?' which distinguishes it from siblings like brand_mentions or topic_sentiment.

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 for fast-growing content and explicitly mentions pricing and error handling, but does not contrast with alternatives or specify when not to use.

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