terminalfeed

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds useful behavioral details: 'Returns JSON', 'No auth required', and 'Cache TTL 60s', which go 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?

Three concise sentences: purpose, return/auth/cache details, and usage context. No wasted words; every sentence adds value.

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

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

For a zero-parameter tool with no output schema, the description fully covers: data components, return format, authentication, caching, and usage intent. No gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters exist, so schema coverage is 100%. The baseline is 3, and the description adds nothing about parameters because none are 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 the verb 'Fetches' and the specific resource: a 'real-time world-state snapshot' composed of multiple data sources (BTC, Fear & Greed, earthquakes, HN stories, ISS crew). This distinguishes it from sibling tools that cover individual data points.

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 use when the agent needs a 'quick global pulse before deciding what to investigate', providing clear context. While it does not name alternatives, the sibling list implies more specific tools exist for deeper dives.

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 indicate readOnlyHint, openWorldHint, idempotentHint. Description adds valuable context: source (Binance with CoinCap fallback), cache TTL (15s), and no auth required. 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 concise sentences covering purpose, data, source, cache, auth, and use case. Every sentence adds value; 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?

Despite no output schema or params, description covers all relevant information: what data is returned, source, cache, auth, and intended use. Complete for this simple fetch 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?

Tool has zero parameters, so schema coverage is 100% trivially. Description mentions 'in USD' but doesn't need to explain parameters. With no params, baseline 4 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?

Clearly states 'Fetches the current Bitcoin price in USD with 24h change, high, low, and volume.' This is a specific verb+resource, and it distinguishes from siblings like tf_crypto_movers which likely covers multiple cryptocurrencies.

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 usage context: 'Use for crypto trading decisions or when the agent needs a fresh BTC quote.' It does not explicitly state when not to use or mention alternatives, but the guidance is clear and sufficient for most scenarios.

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?

Beyond annotations (readOnlyHint, openWorldHint, idempotentHint), the description adds cache TTL scaling per feed window, public domain status, and return format details. No contradiction with annotations.

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

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

Two compact sentences: first explains function and returns, second gives usage guidance. No filler, every word adds value. Well-structured with key info front-loaded.

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 no output schema, the description fully compensates by listing exact fields returned. Covers parameters, behaviors (cache TTL, public domain), and usage guidance. Informative enough for the agent to use 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%, baseline 3. Description adds value by clarifying the purpose of each parameter (magnitude bucket and period) with example options and defaults, plus the TTL scaling effect. Provides context 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?

Clearly states 'Fetches USGS pre-built summary feeds' with specific parameter options (magnitude bucket, period) and detailed return fields. Explicitly distinguishes from sibling tool tf_earthquakes by mentioning 'more granular control'.

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 usage guidance: 'Use when the agent needs more granular control than tf_earthquakes.' Also explains the parameter options and default values, helping the agent decide when to invoke.

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?

Beyond annotations (readOnly, openWorld, idempotent), the description adds valuable behavioral traits: 60s caching, US Government public domain, and US-only coverage. No annotation contradiction; all consistent with a read-only, open data source.

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

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

The description is concise (two sentences) with no wasted words. The first sentence efficiently covers action, filter, and output; the second adds caching, domain, coverage, and use case. Perfectly front-loaded.

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 no output schema, the description lists return fields. It covers caching, data source, geographic scope, and intended use. All dimensions needed for a data-fetching tool are addressed without gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description reiterates the filter parameters (area, event, severity, urgency, status) but does not add meaning beyond what the schema already provides. It does not introduce additional parameter details or syntax.

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 fetches active weather alerts from api.weather.gov, specifying filters and output fields. It uses a specific verb (Fetches) and resource (active alerts), distinguishing it from sibling tools like tf_climate_earthquakes.

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 a use case ('Use for situational awareness on active weather hazards') but does not explicitly mention when not to use or compare to alternatives among siblings. It implicitly guides by stating the data source and coverage.

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, openWorldHint, and idempotentHint. The description adds source (CoinGecko/CoinLore) and cache TTL (30s), providing useful behavioral context beyond the annotations. No contradictions observed.

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 short sentences, each providing essential information: function, data fields, source/cache, and usage guidance. No filler or 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 no parameters and no output schema, the description sufficiently explains what the tool returns (top 15, sorted by 24h change, with price, market cap, % move). The agent can infer the response structure for a simple fetch operation.

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 no parameters, and schema coverage is 100% trivially. The description adds no parameter info, which is appropriate. Baseline is 4 as per rules for zero parameters.

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

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

The description clearly states it fetches the top 15 cryptocurrencies sorted by 24h price change, including price, market cap, and percentage move. This distinguishes it from sibling tools like tf_btc_price (Bitcoin-specific) and tf_fear_greed (sentiment).

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

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

Description explicitly says 'Use when the agent needs to surface notable crypto market moves.' This provides clear context for when to invoke, though it does not explicitly exclude other scenarios or mention alternatives, which is acceptable given the tool's specificity.

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 declare readOnlyHint, openWorldHint, and idempotentHint. The description adds value by specifying cache TTL (2 minutes) and return details (count, most recent quake info). This goes beyond annotations without contradicting them.

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 consists of two concise sentences. The first explains what the tool does, the second provides usage guidance. No redundant 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 zero-parameter tool with no output schema, the description fully explains what is returned (count, magnitude, place, time) and provides caching behavior. It is 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?

There are zero parameters, and schema coverage is 100% (trivial). Per guidelines, baseline is 4. The description does not need to add parameter info, and it does not.

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 that the tool fetches recent earthquakes from USGS with magnitude >=2.5, providing verb, resource, and scope. However, it does not explicitly differentiate from sibling 'tf_climate_earthquakes', which may cause confusion.

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 context: 'Use for disaster monitoring or when the agent needs current seismic activity.' This implies when to use, but no when-not-to-use or alternative tools are mentioned.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true. Description adds value by stating 'Cache TTL 1h', providing extra behavioral context 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?

Two sentences, front-loaded purpose, no filler. Every sentence contributes value: purpose, cache info, usage guidance.

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 tool with annotations covering safety, description covers purpose, data sources, cache, and alternative tool. No gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no parameters, so schema coverage is 100%. Description doesn't need to add parameter info. Baseline score of 4 is appropriate as description adds no param info but none 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?

Description clearly states 'Fetches latest FRED economic indicators' with specific indicators listed (Fed funds rate, CPI, etc.). It distinguishes from sibling tool tf_premium_macro by specifying scope.

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 'Use when the agent needs current US macro indicators' and directs to tf_premium_macro for deeper macro, providing clear when-to-use and when-not-to-use 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?

Adds cache TTL (5min) and source (Alternative.me) beyond annotations. Annotations already indicate readOnly, openWorld, idempotent; description complements them without contradiction.

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

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

Two sentences, no filler, front-loaded with action verb and key details. Every sentence serves a 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?

Covers purpose, output format, source, caching, and usage recommendation. With no parameters and annotations providing safety profile, description is fully 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?

No parameters, schema coverage 100% (trivial). Description adds value by explaining output format (0-100 with labels), which compensates for lack of output 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?

Clearly states it fetches the Crypto Fear and Greed Index value (0-100) with classification label, and provides source and cache TTL. No ambiguity.

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 'Use as a sentiment signal for crypto trading decisions,' providing clear context. Does not exclude alternatives or compare to sibling tools, but overall guidance 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. The description adds valuable behavioral context: source (Frankfurter/ECB-based), cache TTL 5 minutes, and the list of currencies. This goes beyond the annotations without contradicting them.

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. Every fact (currency list, source, cache TTL) earns its place with 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?

For a tool with zero parameters and no output schema, the description provides sufficient context: purpose, scope (16 major currencies), data source, and caching behavior. Annotations cover the safety profile. The description is complete for an agent to decide when to call 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?

The tool has no parameters and schema coverage is 100%, so baseline is 4. The description does not need to add parameter info, and it correctly omits any unnecessary 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 it fetches current foreign exchange rates with USD as base, and lists the 16 major currencies. The verb 'Fetches' paired with the specific resource 'foreign exchange rates' provides precise purpose, distinguishing it from sibling tools that fetch other data types.

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 suggests 'Use for currency conversion or FX-aware decisions,' which gives a clear context of use. However, it does not explicitly mention when not to use or alternative tools among siblings, though the tool's specificity makes this less critical.

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, idempotentHint. The description adds cache TTL 12h, hand-curated source, and explains the meaning of each view option. This adds behavioral context beyond the 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 moderately long but well-structured. It front-loads the main purpose, then lists views, source, and cache. Every sentence adds value, though slightly verbose in view descriptions. 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?

For a tool with one optional parameter and no output schema, the description covers purpose, usage guidelines, view options, data source, cache TTL, and when to use. It is complete for the tool's complexity.

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 only parameter 'view' has 100% schema coverage with enum values. The description elaborates on each enum value: 'summary' gives top 10 combined leaderboard, 'gaps' gives full per-model harness deltas, 'combined' gives normalized ranking, 'raw' returns full graph. This adds significant meaning beyond the enum names.

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 returns snapshot of benchmark scores across four named benchmarks. The verb 'returns' and resource 'snapshot of public agentic-coding benchmark scores' are specific. No sibling tools are related to benchmarks, so it is well distinguished.

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 ends with 'Use when the agent needs to recommend a harness/model combo or explain why two agents using the same model perform differently.' This provides explicit use cases. It does not list when not to use, but the context is clear enough for an agent to decide.

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 indicate read-only, open-world, and idempotent behavior. The description adds cache TTL (10min), data source URL, and that it returns top 15 models sorted by likes over 7 days, providing additional 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 concise (3 sentences) and front-loaded: purpose first, then item details, source and caching, and usage guidance. 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?

For a tool with no parameters and no output schema, the description covers purpose, data structure, source, caching, and usage guidance completely. It provides all necessary context 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 with 100% schema coverage (none). With 0 parameters, baseline is 4, and the description does not need to add parameter information.

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 fetches the top 15 trending HuggingFace models sorted by likes in the last 7 days, specifying verb, resource, and scope. It distinguishes from sibling tools by focusing on open-source AI community trends.

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 use when the agent needs to surface current open-source AI community attention. It does not list exclusions but the context is clear enough to differentiate from other data 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?

Specifies auth header format and zero credit cost, adding context beyond annotations. However, annotation readOnlyHint=false contradicts the described GET 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?

Three concise sentences, front-loaded with core purpose, no extraneous 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?

Complete for a parameterless tool with no output schema; covers purpose, auth, cost, and use case.

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?

No parameters, and schema coverage is 100%. Description adds value by mentioning the Authorization header requirement implicitly.

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

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

The description clearly states it is a GET endpoint returning remaining credits for the bearer token, differentiating it from sibling tools like tf_payment_buy_credits.

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 use for monitoring agent budget, providing clear context. Does not explicitly mention when not to use, but the purpose is distinct from 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 indicate non-read-only, and the description reveals it's a POST endpoint returning specific data. It could elaborate on side effects or transaction costs, but overall clear.

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 with critical information front-loaded: purpose, output details, and usage context. 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?

Lacks explanation of how the memo is used or next steps after obtaining the quote, leaving a gap for a complete payment flow. Adequate but not fully comprehensive.

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 amount_usd with min constraint. Description adds conversion rate context ($1=50 credits) beyond schema, enhancing understanding.

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

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

The description clearly states the tool's function: a POST endpoint that returns a USDC wallet address, memo, and quote for buying credits at a fixed rate, distinguishing it from siblings like balance checks or confirmations.

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 instructs to use as the first step when buying credits, providing temporal and contextual guidance without 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?

The description discloses that it verifies a transfer and returns a token and credit count, which matches the annotations (readOnlyHint=false, openWorldHint=true, idempotentHint=false). It adds context about the token format and cross-redeemability, going 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?

The description is concise (2-3 sentences), front-loaded with the action and purpose, and wastes no words. Every 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 there is no output schema, the description adequately explains the return values (bearer token format and credit count) and the cross-redeemable aspect. It fully covers the essential behavior for a payment confirmation 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 already provides descriptions for both parameters (100% coverage). The description adds value by explaining that the nonce is the memo from tf_payment_buy_credits and that the tx_hash is the on-chain transaction hash, providing context 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 it is a POST endpoint that verifies an on-chain USDC transfer and returns a bearer token plus credit count. It distinguishes itself from sibling tools like tf_payment_buy_credits and tf_payment_balance by specifying it is used after sending USDC.

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: after the agent has sent USDC, with the tx hash and memo from tf_payment_buy_credits. It also mentions the returned token is cross-redeemable on tensorfeed.ai. However, it does not provide explicit when-not-to-use scenarios or 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?

The description states 'GET endpoint' implying idempotent, read-only behavior, but annotations set readOnlyHint=false and idempotentHint=false, directly contradicting the description. This inconsistency severely undermines reliability.

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 yet comprehensive, front-loading the key purpose and including essential operational details without superfluous 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 no output schema, the description fully explains the return format and handles a notable edge case (pre-ledger tokens), making it self-sufficient for invocation.

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 no parameters, but the description adds value by detailing the return fields and behavior for edge cases, compensating beyond the empty schema.

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

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

The description clearly specifies the tool as a GET endpoint returning confirmed USDC purchases with explicit fields (tx_hash, amount_usd, etc.) plus balance/totals. It distinguishes from siblings like tf_payment_balance and tf_payment_confirm by focusing on history.

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?

Describes required authorization, zero credit cost, and edge-case behavior for tokens minted before the ledger. Though it does not explicitly name alternatives, the context is clear enough for an agent to understand when to invoke this 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 indicate readOnlyHint, openWorldHint, and idempotentHint as true. The description adds valuable behavioral context beyond annotations: 'Cache TTL 60s' and 'sorted by 24h volume'. No contradiction with annotations.

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

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

The description is three short sentences, each serving a distinct purpose: function, output content, and usage guidance. It is front-loaded with the core action and is efficient without unnecessary 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?

Given no output schema and zero parameters, the description provides sufficient information: what the tool returns (question, outcomes, volume), caching behavior, and use case. It completely covers what an agent needs to know.

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 has zero parameters and schema description coverage is 100%, so no parameter information is needed in the description. Baseline for zero parameters is 4, and the description does not add anything beyond that.

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 that the tool fetches active Polymarket prediction markets sorted by 24-hour volume, specifying the verb 'fetches' and the resource 'Polymarket prediction markets' with details on included fields (question, outcomes, volume). It distinguishes from sibling tools by focusing on prediction markets, which is unique among the listed siblings.

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

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

The description explicitly states when to use the tool: 'when the agent needs market-implied probabilities on world events (elections, sports, macro).' It lacks explicit guidance on when not to use it or alternatives, but the context is clear.

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

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

Beyond annotations, the description discloses cost (2 credits), cache (5-min), auth (bearer), and output format (structured JSON + system prompt). No contradiction with annotations; all behavioral traits are clearly communicated.

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?

Every sentence earns its place: starts with key message, lists sources, explains output, value proposition, cost, cache, auth. No redundancy; well-organized.

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 zero parameters and no output schema, the description fully compensates by detailing output structure (JSON + system prompt), listing all 13 sources, and providing cost, cache, and auth info. Complete for the tool's purpose.

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?

No parameters exist, so baseline is 4 as per guidelines. The description adds no parameter meanings but sufficiently explains the tool's behavior and output.

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 states a specific verb ('composes'), resource ('upstream sources'), and outcome ('curated world-state snapshot'). It distinguishes from sibling tools by positioning it as 'always start here' and listing specific data sources not covered by individual 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?

Explicitly says 'always start here' and explains it saves making 13 separate calls, implying it should be used first for broad context before drilling down with other tools. Clear when-to-use 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?

Discloses cost (2 credits, $0.04 USDC) and authorization requirement (Bearer tf_live_<64-char-hex>). Explains that it is a screen, not a prediction. Provides details on what each anomaly includes (type, signal, value, baseline, z_score, severity, description). No contradiction with annotations.

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

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

Single paragraph, front-loaded with main purpose, then enumerates anomaly types, distinguishes sibling, and ends with cost and auth. Every sentence adds value with no 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 no output schema, description thoroughly explains what the tool returns (types of anomalies and their fields). Also includes cost and auth, making it self-contained for agent invocation.

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?

No parameters exist (empty input schema, 100% coverage). Description naturally doesn't need to explain parameters, but baseline for 0 parameters is 4.

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 is a 'Premium anomaly screen' that surfaces statistical outliers. It specifies the types: z-score outliers for VIX and 10y treasury, threshold flags for Fear & Greed, crypto market-cap moves, earthquake counts. It distinguishes itself from the sibling tool 'tf_premium_world_deltas'.

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 use case: 'answers is anything statistically unusual right now'. Distinguishes from world-deltas, helping the agent choose between them. Does not explicitly list when not to use, but the purpose is clear enough.

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?

Adds important behavioral details beyond annotations: costs 1 credit ($0.02 USDC), requires Authorization header, and is strict premium with no free trial. Annotations are not contradicted. Minor lack of detail on possible side effects.

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 5 sentences, front-loads purpose, then details. Every sentence adds value, though could be slightly more concise.

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 two optional parameters, no output schema, and complex behavior (premium cost, auth, free alternative), the description covers all essential aspects: what it adds, how to use parameters, cost, auth, and sibling alternative.

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 description adds extra context: history explains the response includes a series.btc_24h array with 24 data points; include lists all possible sections. This goes 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 it is a premium version of tf_briefing that adds Polymarket prediction markets and supports section filtering and hourly BTC chart. It distinguishes itself from the free basic version tf_briefing.

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 when to use: 'Use when the agent needs prediction-market context or recent BTC trajectory in addition to the basic snapshot.' Also mentions the free alternative tf_briefing for when not 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?

Discloses cost (2 credits), 30-min cache, bearer auth, and that data is pre-computed. This adds value beyond annotations, which have readOnlyHint=false and idempotentHint=false. No contradictions; the description appropriately explains the non-idempotent credit cost.

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?

Single paragraph efficiently covers all essential information: purpose, assets, method, output format, benefits, cost, cache, auth. No unnecessary 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?

With no output schema, description thoroughly explains the output structure and content, including the pairs array, NxN matrix, and tags. Complete for this tool's complexity.

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?

No parameters exist; schema coverage is 100%. Description fully explains the tool's operation without needing parameter details, meeting the baseline for zero-parameter tools.

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?

Clearly states it returns a pre-computed cross-asset correlation matrix for 6 specified assets using 30-day Pearson correlations on daily returns. Explicitly lists output format (pairs array and NxN matrix) and distinguishes from sibling tools by its specific functionality.

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?

States 'Saves the agent from fetching 6 historical price series and running the covariance math,' clearly indicating when to use. Also mentions cost, cache, and auth requirements. Could explicitly state when not to use (e.g., if real-time correlations needed), but overall provides good 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?

Discloses the tool aggregates multiple external APIs and costs credits. Annotations (readOnlyHint=false, openWorldHint=true) suggest dynamic behavior; description does not contradict. Could mention if any write operations occur, but none apparent.

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?

Concise but packed with value: purpose, contents, cost, auth, params, alternatives. Each sentence earns its place, though slightly verbose for a tool description.

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?

Fully covers input parameters and hints at output (e.g., series.btc_30d). Lacks explicit output schema but sufficient for tool selection. Complex multi-source tool handled well.

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?

Adds context beyond 100% schema coverage: explains 'Comma-separated symbol filter' and 'daily BTC OHLCV' for history. Enriches parameter meaning without redundancy.

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?

Clearly defines a premium crypto snapshot with specific resources: top 50 coins, Binance ticker, Bitcoin network stats, Ethereum gas. Distinct from siblings like tf_btc_price (single asset) and tf_crypto_movers (winners/losers).

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 'Use this instead of calling CoinGecko + Binance + mempool.space + Etherscan separately.' Indicates cost (2 credits) and authentication requirements, providing clear when-to-use 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?

Beyond annotations (readOnlyHint=false, openWorldHint=true, idempotentHint=false), the description adds valuable behavioral context: it costs 2 credits, uses a 30-minute cache, requires bearer auth, and sources from DefiLlama. This tells the agent about side effects (cost, caching) and dependencies (auth, external API). No contradiction with annotations.

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

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

The description is two sentences: the first clearly states the tool's purpose and output, the second adds operational details (source, cost, cache, auth). Every sentence earns its place, no filler, and the most important information is front-loaded.

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?

The description covers the returned data fields, source, cost, cache, and auth. For a tool with no parameters and no output schema, this is quite complete. It could optionally describe the exact output format or units, but the listed fields (TVL change, market cap, FDV) are sufficiently detailed 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 input schema has no parameters, so the description does not need to add parameter information. Baseline for 0 parameters is 4. The description does not attempt to add any parameter semantics, 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 it is a 'Composed DeFi total-value-locked snapshot' and enumerates the specific data returned (top 50 protocols with TVL changes, top 15 chains, by-category aggregate, gainers/losers). This makes the purpose very specific and distinguishes it from sibling tools like tf_crypto_movers which focus on price movements rather than TVL.

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

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

The description provides context about the tool's purpose ('for crypto research and trading agents') and includes practical details like cost, cache, and auth. However, it does not explicitly state when to use this tool over alternatives (e.g., tf_premium_crypto_deep) or when not to use it, which would be useful given the sibling list.

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 details caching (5-min), cost (2 credits), auth (Bearer required), and the threshold (5 ETH). It does not mention unintended side effects, and annotations (readOnlyHint=false) are not contradicted; the tool may cost credits but is not destructive.

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 core function first, then details. It is slightly long but each sentence adds value, and no 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 no parameters or output schema, the description fully covers what the agent needs: data scope, threshold, cost, cache, auth, and how to use it alongside other tools.

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?

No parameters exist, so baseline is 4. The description adds extensive meaning by explaining the output structure, threshold, bias label, and usage in trading context, exceeding baseline.

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

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

The description clearly states the tool tracks ETH transfers in/out of major exchange hot wallets over the last 3 blocks, with specific tagging (inflow/outflow/inter_exchange) and aggregation. It distinguishes itself from siblings by mentioning 'Pair with tf_premium_whales for context' and noting v1 covers ETH only.

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 frames usage for trading bots detecting regime shifts and suggests pairing with tf_premium_whales. While it does not explicitly state when not to use it, the context is clear enough for an agent to decide.

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 beyond the annotations by disclosing that the tool costs 2 credits ($0.04 USDC), requires Authorization header, and that captured_at is the monitor real check time with a stalled monitor policy. This provides clear behavioral context, and there is no contradiction with the annotations since readOnlyHint=false aligns with a credit-costing operation.

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 front-loaded with 'Premium reliability breakdown'. Each sentence adds value, but it is somewhat lengthy. It could be slightly more concise, but given the complexity of the output, the length is justified.

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 no output schema, the description thoroughly explains the return components (composite score, subscores, trust tier, low_coverage flag) and data source (rolling counts probed every 5 minutes). It also covers cost, authentication, and special cases (stalled monitor), 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?

There are no parameters, so the baseline is 4. The description does not need to add parameter information since the schema is empty. It adds value by explaining what the tool returns, which compensates for the lack of parameters.

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 that the tool provides a 0-100 composite reliability score with subscores, trust tier, and low_coverage flag. It identifies the resource (feed reliability) and the action (breakdown), and distinguishes itself from the free preview by noting the paid tier adds signed receipt and full per-feed breakdown.

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 indicates that the paid tier is for detailed per-feed breakdowns while the free preview offers a top-line table. It mentions the cost and authentication requirements. However, it does not explicitly state when not to use this tool or compare with sibling tools, though the context of 'premium' implies it is for paid users needing detailed data.

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 states 'Immutable past data', implying a read-only operation, but the annotation 'readOnlyHint': false contradicts this. Additionally, it mentions costs and authorization, but the core behavioral trait is misleading due to the contradiction.

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

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

Three sentences, each earning its place: purpose, parameters with constraints, and additional usage guidance (cost, auth, where to get feed IDs). Front-loaded and efficient.

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

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

Despite no output schema, the description explains the return type (daily composite-reliability time-series) and constraints. It also provides the necessary information to use the tool (feed ID source, date bounding). A minor gap is the lack of detail about the response structure, but overall sufficient.

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 the description adds value by specifying the date format (YYYY-MM-DD) and a query window cap (365 days), which are not evident from the schema alone.

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

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

The description clearly states the verb 'Returns' and the resource 'daily composite-reliability time-series for one feed', specifies the feed parameter, and distinguishes it from sibling tools like 'tf_premium_feed_reliability' by providing more detailed usage context.

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 tells the agent to use a free endpoint to get current feed IDs and mentions cost and authorization implicitly. It does not explicitly state when not to use this tool, but the provided 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 transparency beyond annotations: cost (2 credits, $0.04 USDC), 30-min cache, and source (GitHub Search API). Annotations have openWorldHint=true and idempotentHint=false, with readOnlyHint=false. The description doesn't fully align with readOnlyHint (tool reads data but costs credits—potential side effect), but it honestly discloses the cost and cache 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 a single, information-dense sentence that front-loads the main purpose and lists key outputs. Every clause adds value (outputs, source, cost, cache, auth). No waste.

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 tool with no output schema, the description covers what it returns, the source, cost, cache, and auth. It could mention return format (e.g., JSON) but the outputs are described clearly enough. Slightly incomplete but adequate.

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 schema coverage is 100%. The description adds no parameter info because none exist. Baseline for 0 params is 4—the description is appropriately silent.

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 specifies the tool's purpose: returns a composed GitHub developer-attention snapshot with specific outputs (top repos, AI/ML repos, aggregates). It uses specific verbs like 'returns' and names the resource (GitHub developer-attention snapshot). Among siblings (mostly financial/macro tools), this is distinct.

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 is clear: call to get the snapshot. The description includes source, cost, and cache details, but does not explicitly state when to use it vs alternatives or when not to use it. However, zero parameters simplify usage, and the sibling context shows it's the only GitHub-focused tool, so guidance is largely implicit.

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

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

Annotations indicate readOnlyHint=false and openWorldHint=true. The description adds behavioral context: it's a snapshot that costs 2 credits and requires authorization. It doesn't mention side effects, but the snapshot nature implies no writes. No contradiction with annotations.

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

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

Two sentences, no wasted words. First sentence states core purpose, second provides details. Well-structured and front-loaded.

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?

No output schema, but description lists all included data components (FRED series, forex pairs, etc.) giving a clear idea of what the tool returns. Lacks specific structure details but is sufficient for an agent to understand the content.

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 has 100% coverage for one optional parameter. The description explains the meaning: 'adds 30-day historical series' and details what gets populated. This adds value beyond the schema's description.

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

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

The description clearly states it's a premium composed macroeconomic snapshot, lists specific components (FRED series, forex pairs, gold, market context, commodities), and explicitly distinguishes from siblings by saying 'Use this instead of calling 14 different upstream APIs separately.'

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 explains when to use this tool (for a comprehensive snapshot) and suggests an alternative (using specific APIs separately). It also mentions optional history parameter and credit cost. However, it doesn't explicitly state when not to use it, though the context implies that.

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 provide readOnlyHint=false (not read-only) and idempotentHint=false, but the description does not elaborate on any side effects beyond costing credits. It describes output and statistical nature but doesn't address whether the tool modifies state or has other behaviors. With annotations present, the description adds context about the stress override and data sources but falls short of fully explaining behavioral traits. Score 3 for adding some value but not fully leveraging the opportunity.

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 front-loaded with the purpose and list of inputs. It efficiently packs details on output components, comparison with sibling, cost, and auth. However, it could be slightly more structured (e.g., bullet points) to improve readability. Every sentence earns its place. Score 4 for appropriate size and front-loading with room for minor improvement.

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 no output schema, the description fully explains the output: regime label, risk_score, confidence, drivers breakdown with signal values/weights/contributions, stress override conditions, and source data. It also covers cost, auth, and license (not investment advice). This is complete for a tool with zero parameters and no output schema. Score 5.

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 has zero parameters, so schema coverage is 100% by default. The description adds no parameter info because none exists. According to guidelines, with 0 parameters the baseline is 4, but the description excels by providing rich context about what the output contains, which is the only semantical need. Score 5 as the description adds maximum value in the absence of parameters.

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 is a 'Premium regime classifier' that blends four specific signals into a labeled regime (risk_on, risk_off, transition, stress) with risk_score, confidence, and drivers breakdown. It distinguishes from the sibling tf_preview_regime by explicitly listing added features (full drivers, raw inputs, receipt, no rate limit). This meets the 5 criterion of specific verb+resource and 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 compares this premium version with the free preview, indicating when to use this tool (when full drivers, raw inputs, receipt, and no rate limit are needed). It also states cost and authorization requirements. It does not explicitly say when not to use or list alternatives beyond the sibling, but the context is clear. Score 4 for clear context without exclusions.

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 value beyond annotations by disclosing cost (2 credits), authentication requirement (Bearer token), and that scoring is keyword-based rather than LLM-derived. The openWorldHint annotation is complemented by the description noting it's a snapshot, but no details about data freshness or limits are provided.

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 purpose and lists sources, output, use case, cost, auth, and methodology in a logical flow. It is slightly lengthy but each sentence adds value; 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?

Given no parameters or output schema, the description adequately covers the tool's behavior: output fields, sources, use case, cost, and auth. It does not detail output size or rate limits, but it feels complete for the tool's purpose.

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 0 parameters and 100% schema coverage, no parameter documentation is needed. Baseline score of 4 is appropriate as the description correctly indicates no inputs are 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 it provides a 'premium composite sentiment snapshot' aggregating multiple sources (Fear and Greed, VIX, Reddit, Hacker News, Polymarket) and lists output fields. This distinguishes it from sibling tools like tf_fear_greed, which only provides a single index.

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

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

The description explicitly states 'Use to gauge market mood before a trading or research decision', providing clear context for when to use it. While it does not explicitly list alternatives or when not to use, the context is sufficient for an AI agent to understand its role relative to simpler 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?

The description adds significant behavioral context beyond annotations: it costs 2 credits, has a 1-hour cache, requires bearer auth, and cites the data source (DefiLlama). Annotations indicate readOnlyHint=false and idempotentHint=false, but the description does not mention any mutation, which is a minor inconsistency. However, it covers important operational details.

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

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

The description is moderately concise, packing many details into a few sentences. It front-loads the main purpose and then lists outputs and metadata. Could be slightly more streamlined, but it efficiently conveys essential information without excessive 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?

Despite no output schema and no parameters, the description fully covers the tool's data content, use case (crypto buying power indicator), cost, caching, authentication, and source. This makes it self-contained and sufficient for an agent to decide when and how to call 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?

With zero parameters and 100% schema coverage (as the schema is empty), the baseline is 4. The description adds no parameter-level detail but compensates by thoroughly explaining the output structure, which is more relevant given no 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 states the tool returns a composed stablecoin flow snapshot, listing key metrics (top 20 stablecoins by circulating supply, net changes, chains breakdown, etc.) and aggregate data. It distinguishes this from siblings by focusing specifically on stablecoin flows, a unique niche among many crypto 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?

The description mentions that trading agents use stablecoin growth as a leading indicator for crypto buying power, implying a relevant use case. However, it does not explicitly state when to avoid this tool or contrast it with alternatives (e.g., other premium tools). The usage context is provided but not exhaustive.

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 discloses behavioral traits beyond annotations: cost, cache, scanning methods (mempool.space vs Etherscan), and data fields. No contradiction with annotations (readOnlyHint=false implies not read-only, but description correctly does not claim mutability).

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 separate sections for BTC and ETH, front-loading the purpose. It is slightly long but each sentence adds value, including cost and auth details.

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

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

Given no input parameters and no output schema, the description fully covers what the tool does, how it works, what it returns (tx_hash, value, addresses), and additional context (cost, cache, auth).

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?

No parameters exist in the input schema (0 params). Baseline for 0 params is 4. The description adds no parameter information because none 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 it tracks large on-chain transactions on Bitcoin and Ethereum, with specific thresholds (>=50 BTC, >=100 ETH). It distinguishes itself from sibling tools like tf_premium_anomalies by focusing on whale-sized transactions.

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

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

The description explicitly states the tool is useful for trading bots watching institutional flow signals. It provides context on cost (2 credits), cache duration (5 min), and auth (Bearer). However, it does not explicitly state when not to use it or compare to 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?

The description implies a read-only operation ('event-stream endpoint'), but the annotations set readOnlyHint=false, contradicting this implication. This confusion undermines 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 concise (four sentences), front-loaded with the core purpose, and contains no extraneous information. Every sentence contributes useful detail.

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 no output schema, the description adequately explains the event structure (type, timestamp, severity, structured data). It also covers cost, authentication, and caching behavior. Missing explicit return format, but sufficient for general use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'since' is fully described in the schema, and the description adds value by explaining defaults, clamping, and the type of timestamps. Schema coverage is 100%, so baseline is 3, but the added context justifies a 4.

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 'Premium event-stream endpoint for monitor agents' that aggregates events from four specified sources into one time-sorted feed. It lists the sources and contrasts with polling multiple feeds, distinguishing it 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?

The description specifies the query parameter, default behavior, and cache horizon. It implies usage as a replacement for polling separate feeds, but does not explicitly state when not to use it or list 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?

Discloses rate limit (10/IP/day), unsigned response, no auth required, and statistical heuristic disclaimer. Annotations already indicate read-only and idempotent, and description aligns without contradiction.

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

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

Concise yet comprehensive; each sentence adds value. Structure is logical: free preview description, then comparison with paid version.

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?

Fully explains what the tool returns and its limitations. No output schema, but description covers expected fields. Complete for a simple, no-input 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?

No parameters in input schema, so description does not need to add parameter details. Baseline of 4 applies for zero-parameter tools.

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

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

The description clearly states it returns a free preview of the paid regime verdict, listing specific outputs (regime label, risk_score, confidence, driver, one-line why). It distinguishes itself from the paid sibling tool by contrasting features.

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?

Compares directly with the paid tf_premium_regime, giving context for when the preview is appropriate. However, it lacks explicit 'when not to use' guidance beyond the rate limit.

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 provide readOnlyHint, openWorldHint, idempotentHint. The description adds 'Cache TTL 60s', a behavioral detail not in annotations. No contradiction. It could describe the response format or error states, but the added context is valuable.

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: first states purpose with specific services, second gives usage guidance. No wasted words, front-loaded with key info. Ideal conciseness.

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?

No output schema exists, but the description provides enough context for an agent to decide to invoke it (knows it returns status of listed services). Could be more complete by mentioning the output format (e.g., JSON with status per service), but it is sufficient for a 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?

With zero parameters and 100% schema description coverage, the description has no need to explain parameters. The baseline for 0 params is 4, and the description does not detract.

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 uses a specific verb 'Fetches operational status' and lists the exact services (GitHub, Cloudflare, etc.), making it clear what resource is affected. It distinguishes itself from sibling tools like tf_briefing which cover broader or different data.

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 when to use: 'when the agent needs to know if a dependency is up or to explain a recent outage.' Does not mention when not to use or alternatives, but the guidance is clear and direct.

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=true, idempotentHint=true. Description adds valuable behavioral context: source URL, cache TTL 30s, and specific metrics. No contradiction.

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

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

Three sentences front-loaded with purpose, metrics, source, and usage guidance. Every sentence adds value, no 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?

Despite no output schema, the description lists all return values explicitly (current TPS, average TPS, slot, slot time, epoch progress). Completely informs the agent of what the tool provides.

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?

No parameters exist (schema coverage 100% vacuously). Description adds meaning by detailing returned metrics, which is beyond the schema's empty properties object.

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?

Clearly states 'Fetches live Solana mainnet network metrics' and lists specific metrics, distinguishing it from sibling tools which cover different domains (e.g., BTC price, earthquakes, forecasting).

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 'Use when the agent needs to assess Solana network throughput or congestion.' Provides clear context for use, though no explicit when-not-to-use is necessary given no similar tools.

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