bitcoin-signals

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

Annotations already indicate read-only and idempotent behavior. The description adds valuable context by specifying the return structure (date, btc_price_usd, headline, signals_summary[]) and the nature of each summary line (strength, direction, one-line why). It also includes a disclaimer ('Information, not financial advice'), which is a useful behavioral note. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is two sentences long and front-loaded with the core purpose. The second sentence efficiently conveys the return format, content, and a disclaimer. Every sentence adds value; there is no fluff or unnecessary repetition. The example is minimal but does not detract.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a no-argument tool with no output schema, the description provides a fairly complete picture: it names the top-level fields and describes what each signals_summary line contains. It could be more precise about the exact structure of signals_summary items, but the given detail is sufficient for most use cases. The sibling context and annotations further fill in safety and scope.

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 zero parameters, and the schema coverage is 100% (an empty properties object). The description confirms 'Takes no arguments,' which is redundant but harmless. With no parameters to describe, the baseline of 4 applies, and the description does not need to add further parameter details.

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: it returns a machine-readable digest of the whole signal set. This differentiates it from sibling tools that provide individual signal data (e.g., get_etf_flows, get_hiring_signal). The specific verb 'digest' and resource 'whole signal set' make the purpose unambiguous.

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 obtaining a comprehensive daily overview of all signals, as opposed to individual signal tools. It notes that it takes no arguments, making the usage context clear. However, it does not explicitly mention when not to use it or name alternatives, so it stops short of full exclusion 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?

The description goes well beyond the readOnly and idempotent annotations by detailing the response structure and edge-case behavior: when the feed is absent, stale, or unreadable, it says so in status and does not substitute a zero. This gives the agent full visibility into what to expect.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is efficient, front-loading the core purpose and then providing useful details about the response and fallback behavior. The 'Example: {}' is somewhat ambiguous and adds little value, but the overall structure is clear and without unnecessary verbosity.

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?

With no parameters and no output schema, the description carries the full burden of explaining the tool. It covers the source, the time periods, the response fields, and the behavior for missing/stale data, making it completely sufficient for an agent to invoke 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 tool takes zero parameters, and the description explicitly confirms 'Takes no arguments.' The schema is empty with 100% coverage, so there is nothing further to document; the description adds a negligible but clear statement about the lack of input requirements.

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 begins with 'Spot Bitcoin ETF net-flow figures' which clearly identifies the specific resource and the type of data returned. It explicitly states the source and the time periods covered, distinguishing it from sibling tools focused on other 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 implies the tool should be used when Bitcoin ETF net-flow data is needed, and it specifies the exact scope (latest day plus trailing 5/30-day, per fund). It does not explicitly name alternatives or state when not to use it, but the specificity makes the use case unambiguous.

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 mark the tool as read-only and idempotent, but the description adds substantial behavioral detail: data source ('live ATS boards'), exact response structure, derived fields (delta_30d, pct_30d), and the coverage_note/tracked_count behavior for unmatched companies. This enriches the agent's understanding beyond the basic safety hints.

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 serves a purpose: purpose, return format, example, edge case, and disclaimer. It front-loads the core function and organizes information logically, with 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 lack of an output schema, the description thoroughly documents the response object and key fields. It covers usage examples, edge-case behavior, and includes a disclaimer about non-financial advice, making the tool's behavior and limitations clear.

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 single optional 'company' parameter is described with usage examples and response implications: empty object returns all tracked employers, and a non-matching filter produces a coverage_note explaining the name is outside the tracked set. This adds meaning far beyond the schema's 'Case-insensitive company filter'.

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 reports 'Hiring velocity across tracked Bitcoin and crypto-infrastructure employers, counted from their live ATS boards.' This is a specific verb+resource combination that distinguishes it from sibling tools measuring ETF flows, network signals, or sovereign reserves. The title 'Bitcoin hiring velocity' reinforces the purpose.

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 usage context through examples: 'Example: {"company": "coinbase"} for one employer, or {} for every employer tracked.' It also explains behavior for unmatched filters. While it doesn't explicitly mention alternatives, the narrow scope and examples make when to use it evident.

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, covering the safety profile. The description adds value by disclosing the return structure (as_of, hashrate_eh, pct_30d, pct_90d, why, disclaimer) and the 'Information, not financial advice' note, which gives the agent a clear picture of what the tool yields.

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, front-loaded with the core purpose, and efficiently includes the return schema and a disclaimer without fluff. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter, read-only tool with no output schema, the description is complete: it names the return fields and even gives an example. The annotations handle safety, and the sibling context makes the niche clear. No critical information is missing.

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 zero parameters, and the description explicitly states 'Takes no arguments,' aligning with the empty input schema. There is nothing further to explain, so the baseline of 4 for parameterless tools 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 provides 'Bitcoin network hashrate level and trend,' a specific verb+resource that distinguishes it from sibling tools covering different signals (ETF flows, hiring, treasury holdings, etc.). The title reinforces the exact resource.

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 context is clear: use this tool when needing Bitcoin network hashrate data. While it doesn't explicitly exclude alternatives or mention when not to use, the sibling tools are topically distinct, making the intended usage obvious. No exclusions are needed.

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, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations: it details the return structure, explains the source_quality field ('primary = confirmed against the state's own publication; secondary = reported, not confirmed at source'), and includes a disclaimer. This is valuable context that goes beyond the structured 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 well-structured: purpose, return shape, filtering, example, disclaimer. It is information-dense but each sentence earns its place. It is slightly verbose due to the detailed return field listing, but remains readable and front-loaded with the 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?

Without an output schema, the description compensates well by enumerating the return fields (as_of, btc_price_usd, count, sovereigns[], each row's fields) and explaining source_quality semantics. It also covers both filter modes and the full ranking call. It does not explain every field (e.g., 'status' or 'framework'), but overall it gives a complete picture for an AI agent 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?

The input schema already describes both parameters with full coverage (100%), so the baseline is 3. The description goes beyond by providing example payloads ('{"country": "US"}' and '{"tier": 1}') and clarifying that 'tier 1 = largest holders,' which reinforces schema descriptions. It also explains the empty-object call for the full ranking, adding practical usage nuance.

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: 'Nation-state Bitcoin holdings, ranked largest first.' This is a specific verb ('get' implied) and resource, but it does not explicitly differentiate from sibling tools like get_treasury_holdings, which may cover corporate or treasury holdings. The description is clear and unambiguous but lacks direct sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidance with filter examples: 'Filter by country name or ISO code, or by tier. Example: {"country": "US"} or {"tier": 1} — call with {} for the full ranking.' This gives clear context on how to invoke the tool, though it does not discuss when to prefer this tool over alternatives like get_treasury_holdings. The guidance is strong on 'how' but not on 'when vs. siblings'.

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, openWorldHint=false, and idempotentHint=true. The description adds value by disclosing the exact return fields (including 'why' and 'disclaimer'), noting it takes no arguments, and stating it is 'Information, not financial advice.' No contradiction with annotations exists.

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-loading the core purpose, then detailing the return structure, and ending with a disclaimer. The empty example '{}' is the only minor weakness, but every sentence otherwise earns its place. It is concise without sacrificing essential 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?

With no output schema, the description fully explains the return object, including nested top[] structure. It also confirms no arguments are needed, which is the only input consideration. The tool's simplicity (zero params, read-only, idempotent) is matched by a complete and self-contained description.

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 zero parameters, and the baseline for such cases is 4. The description explicitly confirms 'Takes no arguments,' which removes any ambiguity. There is no schema to clarify, so this is fully adequate.

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 Bitcoin held on public company balance sheets, both in aggregate and by largest holder. It differentiates from sibling tools like get_sovereign_reserves and get_etf_flows by specifying 'tracked public companies' as the resource. The return structure is explicitly defined.

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 makes the tool's context clear: use it for corporate Bitcoin treasury data from tracked public companies. It does not explicitly name alternatives or exclusion criteria, but the resource boundary is evident. This is sufficient for a zero-parameter read-only tool.

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, idempotentHint, and openWorldHint=false. The description adds valuable behavioral details beyond those: the exact return shape ({ as_of, btc_price_usd, count, signals[], disclaimer }), the per-signal fields, each signal's own as_of (implying different timestamps), and the caveat 'Observational data; information, not financial advice.' This gives the agent a solid mental model of behavior without contradicting 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 information-dense but well-organized: purpose first, then return structure, then filters/example, then disclaimer. Every sentence adds necessary context, especially given there is no output schema. 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?

With 3 optional params and no output schema, the description fully compensates by detailing the complete response structure and giving a usage example. It also sets expectations about observational/non-financial-advice data. The sibling tools are clearly narrower, reinforcing this tool's aggregating role, making the description complete for an agent to select and invoke it 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 coverage is 100%, so the baseline is 3. The description goes beyond the schema by explaining the filter effect ('narrow by strength, direction or signal type') and providing a concrete example: '{"min_strength": 70, "direction": "tailwind"}'. This example clarifies how parameters combine, adding value over the bare 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 opens with a specific verb+resource: 'Ranked cross-signal feed of CoinBucha's leading, non-price Bitcoin indicators.' This clearly distinguishes it from sibling tools like get_hiring_signal or get_etf_flows, which are individual signal feeds. The purpose is unambiguous and not a tautology.

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 concrete usage context: 'Optional filters narrow by strength, direction or signal type' and 'call with {} for the full ranked list.' It implies this is the go-to for a cross-signal overview, and siblings are individual feeds. However, it does not explicitly name an alternative or say 'use this instead of X when you need a combined view,' so it slightly misses the 'when-not' guidance.

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