fcc-broadband-mcp-server

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

Annotations already declare readOnly and idempotent hints. The description adds valuable context beyond annotations: data source (FCC Form 477), data vintage (June 2021), and that the output is a ranked table. No contradiction 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 four sentences, each adding essential information: purpose, use case, parameter guidance, and data source. It is front-loaded with the primary action and avoids 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?

Given the complexity (6 parameters, 4 enums) and presence of output schema and annotations, the description adequately covers data source, use case, and parameter limits. It could mention output format more, but the output schema likely handles that.

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 descriptions for all 6 parameters. The description adds value beyond the schema by clarifying the use of compare_all_states and the 50-item limit on geography_ids, which reinforces the schema constraints.

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 compares broadband coverage metrics across multiple geographies and returns a ranked table. It gives concrete use cases like 'which counties have the worst broadband access?' and distinguishes from siblings by specifying it compares same-type geographies.

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 on when to use the tool: to compare multiple areas of the same type, especially for BEAD funding prioritization. It also gives constraints on geography IDs (up to 50) and the alternative of using compare_all_states. It does not explicitly state when not to use, but the context is strong.

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 idempotentHint=true, so the agent knows it's safe. The description adds valuable behavioral context: data source (FCC Form 477 as of June 2021), ranking method (by unserved population), and default to rural areas. This goes beyond the annotations without contradiction.

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 with no wasted words. The first sentence states the core purpose, the second positions the tool, and the third provides defaults and data source. It is front-loaded and 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 complexity (7 parameters, output schema exists), the description is relatively complete. It covers purpose, data source, default behavior, and policy context. The output schema handles return values. Minor gaps: no mention of ranking explicitly (though implied), and no guidance on parameter interactions. Still, adequate for a tool with rich structured metadata.

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 seven parameters thoroughly. The description mentions 'speed threshold' and 'state abbreviation' but adds little beyond what the schema provides. The statement about defaulting to rural areas aligns with the urban_rural_filter default. Minimal added value over the 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 finds geographic areas with limited or no broadband coverage at a given speed threshold, ranked by unserved population. It specifies the verb 'finds,' the resource 'geographic areas,' and key modifiers (speed threshold, ranking). The mention of 'core tool for BEAD program analysis' distinguishes its primary use case from 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?

The description provides usage context (BEAD program, defaults to rural areas) but does not explicitly state when not to use this tool or name alternatives among siblings. It adequately describes narrowing by state but lacks decision guidance for selecting between this and other FCC tools.

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 provide readOnlyHint, openWorldHint, and idempotentHint. The description adds context about using the FCC public Geo API with no authentication, which is consistent and helpful. 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?

Two sentences that are concise and front-loaded. The first sentence states the core functionality and outputs; the second provides critical usage context. Every word 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?

Given the existence of an output schema and high schema description coverage, the description is complete. It covers prerequisites, the API used, and authentication requirements, leaving no significant gaps for an agent to misinterpret.

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 detailed descriptions for latitude and longitude (including ranges and sign conventions). The description doesn't add new parameter information beyond what the schema provides, so a 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 converts lat/lng to a 15-digit census block FIPS code plus other geographic identifiers. It uses a specific verb ('converts') and resource ('coordinate to census block'), and distinguishes itself from sibling tools by noting it's a prerequisite for fcc_search_availability.

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?

Explicitly states it is 'the required prerequisite for fcc_search_availability' and mentions 'no authentication required,' providing clear guidance on when to use it. However, it doesn't explicitly state when not to use it or mention 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 indicate readOnlyHint, openWorldHint false, and idempotentHint, which the description does not contradict. The description adds behavioral context by noting the data source (FCC Form 477 as of June 2021) and clarifying it returns summary statistics split by segments, but does not cover all behaviors like rate limits or pagination.

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, front-loads the core purpose, and contains no extraneous information. Each sentence adds value, and the entire text is efficient while conveying all essential details.

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 and full parameter descriptions, the description covers the return value, data source, supported geography types, and use case guidance. It is sufficiently complete for an agent to invoke the tool correctly without 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?

Schema coverage is 100%, and each parameter already has a description in the input schema. The description adds some contextual clarifications (e.g., speed values and BEAD relevance), but it largely reinforces the schema rather than providing substantial new meaning beyond it.

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 a broadband coverage summary for a geography, detailing population with provider counts split by urban/rural and tribal/non-tribal. It emphasizes its role in digital divide analysis and lists supported geography levels, distinguishing it from siblings like fcc_compare_areas or fcc_find_underserved.

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 context for when to use it (primary tool for digital divide analysis) and recommends a specific speed threshold for BEAD policy. However, it lacks explicit when-not-to-use guidance or direct comparisons to alternative tools.

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, idempotentHint=true, and openWorldHint=false, indicating safe, idempotent read operations. The description adds valuable context beyond annotations by noting the data source (FCC Form 477 as of June 2021), which helps an agent understand data freshness. 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 three sentences, front-loaded with the tool's purpose, followed by usage guidance and data source. Every sentence adds value without any redundancy or fluff.

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 existing output schema (indicated by context signals), the description fully covers purpose, parameter retrieval, and data provenance. It is self-contained and eliminates ambiguity for an AI 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 coverage is 100% and already describes the hoconum parameter, but the description adds semantic meaning by explaining it as a holding company number from fcc_search_providers and giving an example ('130152' for Comcast), providing further guidance beyond the 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 returns a national-level coverage profile for a specific holding company, detailing states, technologies, and location counts per speed tier. It distinguishes itself from the sibling tool fcc_search_providers by directing users to that tool for finding valid hoconum values.

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 this tool (to get a provider profile) and provides guidance on how to obtain the required hoconum parameter via fcc_search_providers, offering clear context and an 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 indicate read-only and idempotent behavior. Description adds context on data scope (fixed/mobile availability, challenge data) and that download URLs are included, aligning with 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?

Three sentences front-load the primary function, then add essential context (prerequisites, credentials). No wasted words.

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 main purpose, data types, file metadata, and credentials. Since an output schema exists, detailed return format is not needed. Slightly lacking on how filtering parameters interact, but 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?

Schema covers all 6 parameters with descriptions. The description adds value by explaining the as_of_date parameter's relation to fcc_list_filing_periods and mentioning file metadata, going 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 the tool lists BDC data files for a specific as-of-date, with details on data types and file metadata. It differentiates from sibling tools like fcc_list_filing_periods by mentioning prerequisites.

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?

Explicitly advises using fcc_list_filing_periods first and lists required credentials. Does not provide explicit when-not-to-use scenarios, but context is sufficient.

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?

The description adds behavioral context beyond annotations: Form 477 periods are hardcoded and always available, BDC requires authenticated API credentials, and there is a data gap. These details are consistent with readOnlyHint and idempotentHint.

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 (three sentences), front-loaded with the main purpose, and each sentence adds necessary information 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?

Given the tool has an output schema, the description appropriately covers purpose, usage order, data gap, and credential requirements. It is complete for a list tool with one parameter.

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 covers all parameters, but the description adds value by explaining that include_bdc=true triggers API authentication and returns BDC dates, while false returns only hardcoded Form 477 periods. This clarifies the parameter's effect beyond the 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 returns available data vintages for Form 477 and BDC filing periods, specifies the action (list), and distinguishes from sibling tool fcc_list_downloads by noting it should be called first to determine valid as_of_date values.

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 advises calling this before fcc_list_downloads, explains the two data sources and the data gap, and notes credential requirements for BDC. It lacks an explicit statement of when not to use, 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?

Annotations already declare readOnlyHint=true and idempotentHint=true. Description adds valuable info: data is from June 2021, ISP-reported at block level, and may overstate actual coverage. This is extra behavioral context beyond what annotations 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?

Four concise sentences with no wasted words. Front-loaded with purpose ('Queries broadband providers...'), followed by usage example, prerequisite, and caveat. Each sentence adds necessary 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?

Given the tool's complexity (4 parameters, output schema exists, annotations present), the description covers all essential aspects: what it does, prerequisite, data source, and limitation. It is fully adequate for an agent to understand when and how to use it.

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 baseline is 3. Description mentions the required parameter (block_fips) and hints at filters (consumer, tech_filter, min_speed_down) but does not add novel explanations beyond the schema's detailed property descriptions (e.g., tech code mapping). It contextualizes the parameters but doesn't significantly enhance semantics.

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 queries broadband providers and advertised speeds at a census block, using verb 'queries' and specific resource 'FCC Form 477 data'. It answers a concrete question and distinguishes itself from sibling tools like fcc_geocode_block (which converts coordinates) by focusing on address-level broadband lookup.

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?

Explicitly states prerequisite: requires a 15-digit census block FIPS code and directs to use fcc_geocode_block first. Implicitly advises when to use (for address-level lookup) and notes data may overstate coverage, but does not provide explicit when-not-to-use or compare with alternatives like fcc_search_providers.

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 idempotentHint=true. The description adds meaningful context beyond the annotations: the data source (FCC Form 477 as of June 2021), deduplication behavior, and that results include hoconum identifiers. This helps the agent understand potential staleness and output semantics.

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 at three sentences, front-loaded with the primary purpose, and every sentence adds value. There is no redundancy or 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?

Given the presence of an output schema (not shown but in context signals), the description does not need to explain return values. It covers purpose, usage examples, parameter scoping, data source, and limitations. For a search tool with 4 parameters and rich annotations, this is fully 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?

With 100% schema description coverage, the baseline is 3. The description adds value by explaining tech_filter codes (e.g., '50=Fiber'), noting that name_search is case-insensitive, and stating the limit default. These details aid correct parameter usage beyond the 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: searching ISPs by holding company name, filtered by state and technology type. It provides concrete examples ('which ISPs serve Washington with fiber?') and distinguishes itself from siblings by focusing on provider deduplication and returning hoconum identifiers for follow-up calls to fcc_get_provider.

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 includes explicit examples of queries and notes that geographic filtering is only state-level, with sub-state granularity requiring cross-referencing block data. It also implies a workflow by mentioning follow-up calls to fcc_get_provider. However, it does not directly compare to sibling tools like fcc_search_availability.

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