BRS Signals

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool fetches the current block height but does not reveal any additional behavior such as potential latency, external dependency on mempool.space, or return value format. The description adds minimal value beyond the tool name.

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

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

The description is a single, compact sentence that directly conveys the tool's purpose without any filler. Every word contributes meaning.

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 trivial complexity (no parameters) and the presence of an output schema, the description is sufficient. It confirms the data source (mempool.space) and the specific metric (block height), which is realistic for a simple getter 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?

The tool has zero parameters, so the baseline is 4. The description correctly implies there is no configuration, and no parameter documentation is needed. No additional semantic value is required.

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 the current Bitcoin block height from mempool.space. It uses a specific verb 'get' with a specific resource, making the purpose immediately evident and distinguishable from sibling tools.

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?

No guidance is provided on when to use this tool versus alternatives. It does not mention any specific use cases, prerequisites, or exclusions, leaving the agent to infer appropriate usage from the name alone.

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

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It explains the scoring semantics and return fields, which is useful, but it doesn't mention side effects, permissions, rate limits, or whether the operation is read-only. The 'get' prefix implies read-only, but it's not explicitly stated.

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

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

The description is well-structured: a purpose question, a usage directive, the scoring scale, and the return fields. It's somewhat verbose but front-loaded with the critical 'BEFORE trusting any directional read' warning and uses line breaks for scannability. Every sentence adds meaningful context.

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-parameter tool with an output schema, the description is quite complete. It explains the pairwise scoring, the meaning of high/low agreement, names the three sensor verdicts, and elaborates on shift_brewing. It could mention the sensor sources more explicitly, but the output schema presumably covers type details. The action guidance ('trust vs. wait') completes the picture.

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 vacuous, so the baseline is 4. The description adds no parameter details (there are none to add), which 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's purpose: measuring agreement among three independent sensors. It uses a concrete question ('How much do the three independent sensors agree right now?') and frames it as a pre-flight check before trusting directional reads, which distinguishes it from sibling tools like get_directional_bias or get_regime_current.

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 explicit usage context: 'Call this BEFORE trusting any directional read.' It also explains how to interpret results for action (high agreement = act, low agreement = wait). However, it doesn't explicitly name alternatives or say when not to use the tool, so it falls short of a 5.

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

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

With no annotations, the description carries the behavioral burden and delivers detailed return-contract information: regime_meta, signal, funding, suppressed, cycle_context, options_context, including conditional 'if available' fields. It does not explicitly discuss side effects or errors, but as a read-only dashboard getter the return details provide sufficient transparency.

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 with a short usage comparison, a bundle summary, and a bulleted return list. The only redundancy is repeating 'full picture in one call' at the start and end, which slightly detracts from conciseness but does not undermine clarity.

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 aggregate endpoint with an output schema, the description is complete: it explains the tool's scope, names the bundled data sources, lists the returned fields, notes conditional availability, and gives usage context relative to siblings. Nothing needed for correct selection or invocation 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, so the input schema is already fully covered. The rubric baseline for 0 parameters is 4; the description appropriately spends no space on parameter details and instead documents what the caller receives.

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 identifies this as an aggregate dashboard endpoint: 'The full picture in one call' and 'Bundle: regime + signal + funding + suppressed signals.' It names the specific resource and distinguishes it from the individual sibling tools that return single answers.

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?

Explicit guidance is given: 'Use the individual tools when you want one answer cheaply; use this when you want everything at once.' It directly addresses when to prefer this tool over the alternatives, which are its 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?

With no annotations provided, the description carries the full burden. It discloses the authentication requirement (Pro API key or x402), explains the confidence normalization (0.30–0.50 range), and notes that suppressed signals are shown, not hidden. It does not explicitly mention read-only behavior or rate limits, but the given context is substantial.

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

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

The description is a bit longer than the minimal two-sentence ideal, but every sentence adds value: purpose, return fields, WAIT interpretation, confidence normalization, suppression behavior, and auth/alternative. It is front-loaded with the key question. Minor verbosity keeps it from a 5.

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 that there are no parameters and an output schema exists, the description need not detail return types further. It provides the necessary interpretive context (WAIT meaning, confidence comparison, suppression flag) and access requirements, making it complete for an agent to decide when and how to use 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?

The tool has zero parameters, so the baseline is 4. The description adds no parameter detail (unnecessary), but the output semantics are explained well. This is appropriate for a no-input tool.

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 clear, specific purpose ('Should you be trading Bitcoin right now, and in which direction?') and details the output fields (side, confidence, regime, zone, reason, price, timestamp). It distinguishes itself from the sibling get_convergence by mentioning it as a free alternative.

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

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

It explicitly names get_convergence as a free alternative, implying when to use that instead (when no Pro API key/payment). It also gives guidance on interpreting WAIT ('respect it; do not force a trade'), which helps the agent decide when to act on the result.

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

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

With no annotations, the description carries the full behavioral burden, and it does well: it calls itself a raw read, identifies the free-key access tier, and explains every returned field's meaning and interpretation. It conveys that this is a non-mutating analytical read. It could add caveats about data freshness or edge cases, but it is substantially transparent for a zero-parameter read tool.

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 a strong purpose statement and organizes return fields clearly. However, the final paragraph repeats curve-type interpretations already listed in the Returns section, adding redundancy. It is not bloated, but a couple of sentences do not earn their 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 tool with no input parameters and no annotations, the description is largely complete: it explains the access tier, the output fields, and the interpretive meaning of each curve type. It omits any note on data recency or limitations, but an agent can call and interpret the tool correctly from the provided 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?

The tool has zero parameters, so there is nothing for the description to explain about invocation arguments. The baseline for a zero-parameter tool is 4, and the description appropriately focuses on output semantics instead of trying to document nonexistent inputs.

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 identifies the tool as a raw read of the mempool fee histogram and summarizes who is transacting on-chain right now. It names concrete output concepts like curve_type and actor_profile, going well beyond a restatement of the tool name. It does not explicitly differentiate itself from siblings such as get_mempool_fees, but the core purpose is 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 implies usage context: use it to understand current on-chain actor behavior and fee-curve shape. It offers rich interpretation of curve types, which helps an agent decide what the result means. However, it never states when to prefer this tool over siblings like get_mempool_fees or get_mempool_stats, leaving some selection ambiguity.

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

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

With no annotations, the description carries the behavioral burden. It explains that the signal is necessarily contrarian, that high squeeze probability reflects crowded positioning, and enumerates all returned components including direction and velocity. It does not disclose limitations such as calculation windows or error behavior, but for a read-only, zero-parameter indicator it gives meaningful behavioral context.

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 front-loaded with the core question and formatted with a clear returns list, but it repeats the contrarian/reversal point twice ('Contrarian by design' and the closing paragraph). Some of the return-field detail may duplicate the output schema, making the text longer than strictly necessary.

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-input tool with an output schema, the description is largely complete: it defines the signal's interpretation and all return values. It does not specify time windows, units for max_spread, or data-source caveats, but these are minor given the tool's simplicity and the presence of an output schema.

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 accepts zero parameters, so the baseline is 4 and the description need not add parameter meaning. It instead explains what each returned field represents, which is useful given the empty input 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 identifies the tool as a cross-exchange funding divergence/squeeze indicator, with a specific question and concrete outputs (squeeze_probability, divergence_direction, max_spread, spread_velocity, per-exchange rates). It does not explicitly differentiate it from similarly-named siblings such as get_convergence or get_directional_bias, so it stops short of a 5.

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?

Usage context is implied by the framing question 'Is positioning one-sided enough to squeeze?' and the warning that high squeeze probability is contrarian, but the description never states when to prefer this tool over the alternative signals in the sibling list, nor does it give a when-not-to-use condition.

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

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It explains the output semantics and how to interpret gamma flip levels, but does not mention read-only nature, data latency, subscription requirements, or any side effects. This is a significant gap for a tool with no annotation coverage.

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, front-loading the core concept in a question, then providing a brief summary and a clear Returns list. Every sentence adds value without being overly verbose. The format is scannable 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 that the tool has no parameters and an output schema exists, the description provides sufficient interpretive context (e.g., gamma flip as magnetic level, negative gamma meaning explosive potential). However, it omits details about data source, update frequency, or any caveats about the Pro stream, which would improve completeness.

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, so the baseline for parameter semantics is 4. The description correctly focuses on outputs and interpretation, and there is no schema information to supplement. It effectively communicates that no input configuration is needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly explains the tool's purpose: it returns net dealer gamma, gamma flip level, and related metrics. It distinguishes from siblings by focusing specifically on gamma exposure, making it clear this is about dealer hedging dynamics. The opening question is slightly indirect, but the Returns list makes the purpose explicit.

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 assessing whether dealer hedging amplifies or dampens moves, but it does not explicitly state when to use this tool versus alternatives like get_directional_bias or get_funding_divergence. No exclusions or alternative recommendations are provided.

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

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

With no annotations provided, the description carries the full burden. It adds useful behavioral context by explaining fee thresholds (above 50 = congestion, above 100 = extreme) and noting the live data source, which goes beyond a simple read-only description.

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 concise sentences, front-loaded with the main purpose, and every sentence adds value: source, return values, threshold interpretation, and use-case context.

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 simple, parameter-free read tool with an output schema, the description covers the essential elements: source, output format, interpretation, and use case. It lacks details on data freshness or error responses, but these are minor gaps for this tool's simplicity.

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, so the baseline is 4. The description appropriately focuses on output semantics rather than parameter details, as no parameters exist to explain.

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 live Bitcoin mempool fee rates and specifies the exact outputs (fastest, half-hour, economy, minimum in sat/vB), which distinguishes it from siblings like get_mempool_stats and get_fee_histogram.

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

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

It identifies a specific use case ('Critical for X-Ray sensor verification') but does not explicitly compare to alternatives or state when not to use this tool. Implied usage is available, but no exclusion or direct sibling comparison is provided.

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

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

With no annotations provided, the description carries the full burden. It discloses the specific stats and units (vbytes, BTC) and gives advisory thresholds, which is helpful. However, it does not mention data source, caching behavior, or whether stats are cumulative, which are useful for a read tool. The transparency is adequate but not rich.

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

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

The description is exceptionally concise, with a clear front-loaded summary of the three main stats, followed by a brief, useful interpretive note. Every sentence provides value and there is no redundant phrasing.

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 stateless, parameterless tool, the description sufficiently covers the output fields and basic interpretation. The presence of an output schema means return values don't need detailing. However, the lack of usage guidance and any mention of 'live' semantics or update frequency leaves minor gaps for a complete picture.

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?

There are zero parameters, so parameter semantics are trivially covered by the empty schema. The baseline for 0 params is 4, and the description does not need to add anything beyond what is already given. It correctly identifies the tool's self-contained nature.

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 live mempool stats including pending transaction count, total size in vbytes, and total fees in BTC. This is a specific verb+resource combination and effectively distinguishes it from sibling tools like get_mempool_fees, which likely focuses on fee details.

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?

No guidance is given on when to use this tool versus alternatives such as get_mempool_fees or get_dashboard. The description only provides interpretive thresholds for congestion/quiet, not usage context. It does not mislead but offers no comparative direction.

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

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

With no annotations, the description carries the transparency burden. It discloses that the tool is free and requires no key, and describes the conceptual output (regime events with conviction and duration). It does not mention rate limits or edge cases, but the output schema fills in return structure.

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 serving a purpose: an engaging hook, the return details, usage timing, and access note. It is front-loaded with the core purpose and wastes no 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?

For a parameterless tool with an output schema, the description provides complete context: what the regime represents, how to use it in a workflow, and access requirements. The output schema covers the specific return fields, so no additional detail is needed.

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, so the baseline is 4 per the rubric. The description appropriately focuses on output semantics rather than parameter details, which are nonexistent.

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 the current market regime (accumulation, distribution, trending, reorganizing) with conviction and duration, as latest_event plus all active_events. This distinguishes it from sibling tools like get_directional_bias or get_convergence, which focus on individual metrics.

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

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

It explicitly advises using this tool to pick the playbook (trend-following vs mean-reversion vs sit out) BEFORE interpreting any individual reading, providing clear contextual guidance. However, it does not name specific alternative tools for contrast, so it misses the top score.

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

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

With no annotations provided, the description carries the full disclosure burden and does substantial work: it flags 'public' access, documents the parameter precedence rule ('days... Ignored if day set'), and enumerates return contents. It does not explicitly declare read-only/no-side-effect behavior or mention rate limits/freshness, though 'Returns...' strongly implies a pure read.

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?

At roughly 110 words with a clear Purpose → Args → Returns structure, the description is efficient and front-loaded with its core purpose. The 'Why no signal?' hook and the 'prove it' quote are slightly redundant with the following sentence, keeping it just shy of maximally lean.

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?

Because an output schema exists, the description need not fully explain the return shape, yet it still lists the key fields (cycles_total, emitted, signals_sent, gate_order). All three optional parameters are fully documented and access context ('public') is stated, so an agent can select and invoke the tool correctly; only timing/freshness behavior is left unspecified.

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 0%, yet the description documents all three parameters with formats (YYYY-MM-DD), default behavior ('Empty = today'), the interaction rule ('Ignored if day set'), and the special 'launch' keyword for since. This fully compensates for the bare schema and exceeds what any structured field 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 specifies a precise verb+resource pair: it 'returns the cycle count at each gate in order' so the agent can 'draw a survival funnel and see where reads are being rejected.' The funnel/rejection framing clearly distinguishes it from all 15 sibling analytics tools, none of which target pipeline drop-off.

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 a concrete trigger scenario: it is 'the direct answer to "BRS rejects almost everything, prove it"' and frames the tool as the explanation for 'Why no signal?' This is clear context for when to call it. However, it names no sibling alternatives and gives no explicit when-not-to-use guidance, so it stops short of a 5.

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

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

With no annotations, the description carries the full burden and does well: it discloses Pro-tier access, that outcomes are measured at +4h/+24h with worst drawdown, and that outcomes are never re-scored after the fact. This gives the agent an honest integrity-oriented picture, though it does not cover ordering or rate-limit behavior.

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 and keeps the detail organized. The sentence about get_rejection_funnel's sibling REST endpoint is awkward and slightly tangential, but overall the text is compact and information-dense 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?

An output schema exists, so return-value structure is covered elsewhere. The description documents the single parameter, clarifies the data fields, states the access tier, and gives an alternative access path. Minor details like sorting or pagination are absent but not critical for this simple 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?

The schema only provides an integer limit with a default and no description. The description's Args section adds real meaning: 'Number of recent signals to return (1–200, default 20)' gives a range and semantics that the schema lacks, fully compensating for the 0% schema coverage.

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 'Recent calls and what Bitcoin did next (Pro tier)', naming a specific resource, time scope, and what the data enables. It clearly distinguishes this signal-history tool from the market-data siblings like get_fee_histogram or get_mempool_stats.

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

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

It gives clear context: use it to verify rather than trust, and it notes that a keyless public track record exists on the website/REST endpoint as an alternative. It could be more explicit about when not to use this tool versus sibling tools, but the Pro-tier gating and public alternative serve as practical 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?

With no annotations, the description carries the burden of disclosing behavior. It details the return fields (flow_regime, total_net, transfers) and explains their meaning, providing good transparency. It does not mention rate limits or authentication, but for a read-only indicator tool this is sufficient.

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 somewhat repetitive—the final paragraph restates the first paragraph's point about inflow/outflow pressure. While the 'Returns' list provides structure, the duplication makes it less concise than ideal.

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-parameter tool with an output schema, the description is fairly complete: it explains what the tool returns and how to interpret the signs. It lacks detail on time ranges or data sources, but these are not critical for basic usage.

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 input schema is empty (coverage 100%). Per the baseline for 0 params, a score of 4 is appropriate; there is no parameter semantics to add.

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 detects whale buying/selling intent via stablecoin flows, with a specific verb ('detect') and resource ('stablecoin flows'). It distinguishes itself from sibling tools by focusing on pre-exchange flow analysis and specifying USDT/USDC movements.

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 monitor buying/selling pressure before it hits exchanges) and explains the interpretation of inflow/outflow surges. However, it does not explicitly mention exclusions or alternatives, which prevents a 5.

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

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

With no annotations, the description must carry the behavioral burden. It clearly explains that this is a read-only operation returning three counters and defines each counter's meaning. It does not mention side effects, rate limits, or other behavioral traits, but for a simple getter with no parameters and no mutation, the return-value explanation is sufficient. The description adds value by explaining what each number represents.

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

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

The description is compact and well-structured: a one-line hook, a one-line cautionary note, and a three-line bulleted return list. Every sentence earns its place. There is no fluff, and the format makes the return fields easy to parse. It is front-loaded with the most important context ('sample size behind everything').

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 getter with an output schema, the description is complete. It explains the purpose, the use case, and each returned field's meaning. There is no missing information about authentication, side effects, or prerequisites. The output schema likely provides the structured return types, so the description doesn't need to redisclose them. This is fully sufficient for an agent to invoke and interpret results.

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, so the schema is trivially complete (100% coverage). The description doesn't need to add parameter semantics. Per the rubric, a baseline of 4 is appropriate for zero-parameter tools. No additional information about inputs is needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states what the tool does: it returns sample-size related counters (signals_fired, data_points, days_collecting). It distinguishes itself from siblings like get_system_health (health status) and get_signal_history (historical data) by focusing on the aggregate counts behind the system's data collection. The specific verb 'returns' plus the explicit output list makes the purpose unmistakable.

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 an implicit usage context: 'Small samples cannot prove an edge — this tells you exactly how small.' This tells the agent when to use this tool (when assessing data sufficiency). It does not explicitly name alternatives or exclusions, but the context is clear enough for a zero-parameter getter. It could have explicitly said 'use this to check sample size before making conclusions,' but the intent is embedded.

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

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

With no annotations, the description carries the full burden. It adds behavioral context by explaining that a sensor being down affects the meaning of every other answer, which is beyond a simple status report. It does not discuss side effects, but for a health check, read-only behavior is implied.

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 immediately engaging and front-loaded with a question, followed by a concise component-by-component explanation. Every sentence adds value 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 no parameters and an output schema exists, the description is fully sufficient. It explains what the tool does, when to use it, and why it matters (downed sensor changes other answers), making it complete for an 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?

The tool has zero parameters, so the schema is empty. The description adds no parameter info, but with no parameters, the baseline of 4 applies.

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: checking if the instrument is operational right now, with component-by-component status for all collectors and engines. This distinguishes it from sibling tools that fetch specific data (e.g., get_block_tip, get_gamma_exposure).

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 when-to-use guidance: 'Call this first if any reading looks stale or absent.' It does not name alternatives or when-not-to-use, but the directive is strong and contextually clear.

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

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

With no annotations, the description carries the burden. It states 'read-only' and 'Returns: JSON array of result rows', covering non-destructiveness and return format. However, it does not disclose potential limitations like execution time, result size, or query complexity restrictions, leaving moderate transparency.

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 and succinct, with a clear opening definition, table list, usage sentence, Args block, and Returns line. Every part is informative and there is no 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?

For a tool with one parameter and an output schema, the description covers the core essentials: purpose, tables, usage, parameter semantics, and return format. It lacks an explicit note about preferring get_* endpoints for standard metrics, but overall it is sufficiently complete for this simple 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?

The input schema offers no parameter description (0% coverage). The description compensates with an Args section clarifying 'query: SQL SELECT statement to execute', which adds meaning by specifying the parameter must be a SELECT query and providing database context. This goes beyond 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 clearly states 'Run a read-only SQL query against the BRS SQLite database' with a specific verb and resource. It lists tables and example uses, effectively distinguishing it from sibling get_* tools by presenting it as a raw SQL interface.

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 'Use this to check signal history, regime state, or system health without spawning sqlite3 CLI commands,' providing clear context and a contrast to the CLI alternative. However, it does not explicitly mention when to prefer the specialized get_* tools, so it lacks exclusions.

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