fcc-broadband-mcp-server

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the tool is safe and idempotent. Description adds important context: data is from FCC Form 477 as of June 2021, and the output is a ranked table sorted by unserved or underserved population. 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 three sentences: first states purpose and output, second gives example use case, third provides usage instructions. It is concise and front-loaded, but could be slightly more structured (e.g., bullet points for parameters). No 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?

Given 6 parameters with many enums, the description covers all essential aspects: purpose, output, usage options, data source, and date. Output schema exists but is not shown; description mentions ranked table. Slightly incomplete on return format, but adequate given annotations and schema richness.

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. Description adds significant value by explaining each parameter's nuance: sort options tie to BEAD funding, speed thresholds have typical use cases (25 FCC legacy, 100 BEAD), tech_filter expands acronyms, geography_ids has max 50, and compare_all_states overrides IDs.

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 compares broadband coverage metrics across multiple geographies and returns a ranked table. It answers a specific question and differentiates from siblings like fcc_find_underserved (which focuses on finding underserved areas) and fcc_get_coverage_summary (which likely covers a single area).

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 says to provide up to 50 geography IDs or set compare_all_states=true, and specifies data source and date. It implies use for comparing multiple areas of same type. Does not explicitly mention when NOT to use it, but the context is clear given sibling 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, so safety is clear. Description adds valuable context: data source (FCC Form 477 June 2021), default rural filtering, ranking by unserved population. This enriches behavioral understanding beyond 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?

Four sentences, all substantive. Front-loaded with purpose, then key usage hints. No redundant or unnecessary text.

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 7 parameters with full schema descriptions and an output schema present, the description covers usage context, data source, defaults, and policy relevance. Complete for a read-only query 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%, and each parameter has its own description. The tool description adds overarching context like default rural and nationwide mode but does not significantly enhance individual parameter semantics 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?

Description clearly states verb 'Finds geographic areas with limited or no broadband coverage' and specifies ranking by unserved population. It positions the tool as core for BEAD program analysis and broadband equity research, distinguishing it from siblings like fcc_compare_areas or 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?

Description tells when to use (for underserved area analysis, BEAD) and provides context like optional state abbreviation for narrowing scope and default rural focus. It lacks explicit when-not-to-use or alternative tool mentions, but 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 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 already declare read only and idempotent. Description adds data source (FCC Form 477 as of June 2021) and typical output structure. 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?

Four sentences, each adding distinct value: function, positioning, supported geographies, data source and policy tip. No 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?

Given output schema exists, description is sufficient: covers purpose, segmentation, geographies, data source, and policy context. Lacks error handling or performance notes but not critical for a read-only 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 covers 100% of parameters. Description adds value with usage tip for speed_down and highlights geographic scope. Above baseline 3 due to extra context.

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 returns a broadband coverage summary with provider counts split by urban/rural and tribal/non-tribal segments. It is positioned as the primary tool for digital divide analysis, 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?

Provides explicit recommendation to use 100 Mbps for BEAD policy analysis, and calls out it's the primary tool for equity analysis. Does not explicitly list when to avoid, but context from sibling tools provides implicit guidance.

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.