rate-hub

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

With no annotations, the description carries the full burden and does so thoroughly: daily refresh with an as_of timestamp, coins appearing in multiple categories, partial coverage (only larger coins listed), omitted empty categories, result-size growth characteristics, and both error states (503 not_ready before first refresh, not_found when categories are disabled). This is unusually complete disclosure of mutation-free but non-obvious 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?

Front-loads what the tool returns before adding cadence, join guidance, and error semantics. Every sentence carries information, though the parenthetical about 'both copies of the result' is slightly convoluted and could be tightened.

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

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

An output schema exists, so return-value structure need not be restated, and the description fills the remaining gaps: freshness, coverage limits, join semantics, and failure modes. Nothing an agent needs to call and interpret this tool correctly is missing.

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

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

The tool takes zero parameters, so there is nothing for the description to disambiguate; baseline for a parameterless tool is 4. The description correctly implies a no-argument call.

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?

Names a specific resource (coin categories) plus the underlying endpoint, and states exactly what each entry contains (category id/name plus catalog uids). It also distinguishes itself from siblings by naming get_rates and search_assets as join partners rather than alternatives, so an agent can place it precisely in the toolset.

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?

Gives a concrete usage path — join category uids with uids from get_rates or search_assets to group holdings — and explains the daily refresh cadence. It stops short of stating when NOT to use it (e.g. vs get_meta or get_daily), so it is clear context without explicit 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?

With no annotations present, the description carries the full burden and does so well: it discloses the immutable archive semantics, the meaning and finality of out_of_range, the transient/retry-after nature of pending, the unsupported-uid case, where limits live (get_meta), and points to ratehub://docs/behavior for remaining edge cases. An agent can reason about failure handling before the first call.

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?

Purpose and endpoint are front-loaded, and every sentence is substantive rather than filler. The status enumeration is crammed into dense run-on prose that would scan better as a list, and it partly restates the item 'status' field that the output schema already defines.

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 three-required-array-param request against a rich output schema, the definition covers what the agent needs: scope, day constraints, per-item status semantics with retry policy, limit location, and a documented resource for deeper edge cases. Since an output schema exists, the description is not obliged to explain the response envelope further.

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 schema already documents assets as 'Catalog uids', vs as 'Quote currencies', and days as past UTC YYYY-MM-DD. The description restates the day format/UTC constraint but adds no extra parameter meaning (e.g. what a catalog uid is, how to obtain one, or per-call limits beyond a pointer to get_meta), so this sits at the 3 baseline for schema-documented params.

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?

States a specific resource with clear qualifiers: immutable daily rates for catalog uids against quote currencies on UTC calendar days, plus the backing endpoint (POST /v1/rates/daily). The 'daily' granularity and 'immutable' property separate it from granularity-flexible siblings like get_rates and get_series without needing to open their schemas.

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?

Gives strong operational context: days must be past UTC dates, crypto history beyond ~a year comes from daily candles (source s8), and each status tells the caller whether to retry (pending), never retry (out_of_range), or treat the result as final (ok). It never explicitly contrasts this tool with siblings such as get_rates or get_series, so selection between them is left to inference.

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

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

No annotations are provided, so the description carries the full behavioral burden. The embedded 'GET /v1/meta' implies a read-only operation, but nothing is said about auth requirements, caching/TTL, response size, or whether the values are static or live — all relevant for a metadata endpoint.

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?

A single tight sentence lists the payload contents first and appends the endpoint, with no filler or restatement of the tool name.

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

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

For a zero-parameter read tool, the description plus the existing output schema covers what is returned, so return-value detail is not required. It is nonetheless silent on usage context, which leaves the agent to infer why and when this endpoint should be hit.

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

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

The tool takes zero parameters, so there is nothing for the description to disambiguate; the schema is trivially complete.

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 names the concrete content returned — API limits, supported quote currencies, and build info — plus the backing endpoint GET /v1/meta, which is specific enough to separate it from the market-data siblings. It stops short of a clean verb+resource framing, but an agent can still tell this is the configuration/metadata endpoint.

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?

There is no statement of when to call this versus get_rates, get_daily, or the other data tools, and no prerequisites or typical ordering (e.g. call first to discover quote currencies and limits). The parenthetical endpoint path is the only routing hint.

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

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

With no annotations, the description carries the full burden and does so unusually well: it defines per-rate status semantics (ok/stale/pending/unsupported), retry timing, the 7-day dynamic-coin expiry, uid churn after reset, payload sizing, and how an optional source never falls back to another source's price. Only auth/permission context is absent, which is minor for a read-style pricing call.

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?

Front-loads purpose and endpoint before the detail, and nearly every sentence carries operational information. It is nonetheless dense to the point of sprawl, with a slightly redundant parenthetical about the dual text/structuredContent payload and some overlap with the schema's own source 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?

An output schema exists, so return rendering need not be covered, yet the description still explains the meaningful response semantics (status values, decimal-string prices, size). Combined with limit and expiry behavior, an agent has everything needed to call this correctly.

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

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

Schema coverage is 100%, so the baseline is 3, and the description earns above that by explaining the class-ranged uid encoding (uid = class*1e8+seq with concrete examples), the per-source 'unsupported rather than substitute' rule, and the size cost of include=sparkline. Most of this meaning is genuinely additive rather than restating 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?

Opens with a specific verb+resource+endpoint: "Batch indicative prices for catalog uids (POST /v1/rates)." It also differentiates from siblings by routing uid discovery to search_assets and limits to get_meta, so the agent can place it in the toolset without reading other schemas.

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?

Gives concrete when-to-use guidance: use search_assets to find uids, request sparkline "only when needed," consult get_meta for limits, and read ratehub://docs/behavior for edge cases. It lacks explicit when-not-to-use or routing against the historical sibling get_series, so it stops short of a 5.

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

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

With no annotations, the description carries the full burden and does so: it enumerates the status contract (ok/stale/pending/unsupported), states that upstream failures surface as stale with fetched_at, defines unsupported as a non-error empty result, explains cold-range backfill needing retries, and gives a byte-per-point size estimate. This is far more than the schema can convey.

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?

Purpose is front-loaded in the first clause, then details follow in a logical order (coverage, status contract, sizing, edge cases). The single dense paragraph of grey text is longer than ideal and stacks several clauses per sentence, but nearly every clause carries actionable 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?

An output schema exists, so return values need not be explained, yet the description still documents the meaningful shape (points, status, fetched_at, retry_after) and points to a further resource for behavior notes. Combined with per-range coverage rules and edge cases, an agent has everything needed to call it correctly.

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

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

Schema coverage is 100%, so the baseline is 3, but the description adds genuine meaning: max_points downsamples evenly while keeping the first and last point, max_points=500 yields full detail, range is clipped for non-USD fiat quotes, and uid can effectively expire for dynamic coins. It clarifies behavior the schema alone cannot express.

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?

States a specific verb and resource ("Price history of one asset for charts") plus the underlying endpoint, which distinguishes it from siblings like get_daily, get_rates and get_top that also return numeric series. An agent can tell this is the per-asset chart-series tool without opening the schema.

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?

Gives rich conditional guidance on coverage: crypto supports every range, fiat only 1d/7d from rate-hub and the rest from the daily archive. It also tells the agent what to do on 'pending' (retry after retry_after) and how to read 'unsupported'. It never explicitly compares against sibling tools such as get_daily, so it stops short of full alternative routing.

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

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

With no annotations, the description carries the burden and does so well: it discloses payload size (~1 KB per asset with market), the default of 20 and max of 250, that unpriced-in-quote items are silently skipped, and it routes edge cases to the ratehub://docs/behavior resource. Rate-limit and auth behavior are only pointed at rather than stated, keeping it below a 5.

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?

Front-loaded with the core purpose, then size, defaults, and edge cases in a logical order with no filler sentences. It is slightly cramped and mildly redundant, restating the market block contents after already naming it.

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

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

An output schema exists, so return values need not be explained, and the description instead supplies the operational facts an agent needs: defaults, caps, compaction flag, skipped-item behavior, and a pointer to deeper behavioral docs. Complete for a three-parameter listing tool, with only rate limits/auth left implicit.

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

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

Schema coverage is 100%, so the baseline is 3, but the description genuinely adds meaning: include_market is tied to the ~1 KB size figure and 'both copies of the result', and the default limit behavior is restated in context. That is more than a paraphrase of the schema, though it doesn't explain the `vs` quote-currency mechanics in any depth.

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+resource ('Top crypto assets by market rank') and adds the underlying endpoint GET /v1/top, so the operation is unambiguous. It does not, however, differentiate itself from the sibling search_assets or get_series, leaving the agent to infer which listing path applies.

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 implied rather than stated: 'pass include_market=false for a compact list' and the default/max limit tell the agent how to tune a call, but there is no when-to-use-this-vs-search_assets guidance and no explicit exclusions. Adequate but with a clear gap around 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?

With no annotations, the description carries the full burden and does so richly: payload size (~0.4 KB/asset), default pagination limit and page-size bounds, that limit=0 is an error, that candidates lists at most 5 unregistered coins, that dynamic coins expire after 7 days and may change uid, and that asset.icon is a placeholder. It even routes to a behavior-notes resource for edge cases.

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?

Front-loads purpose and then packs filters, sizing, pagination, and edge cases into dense but non-redundant sentences. It is long and somewhat wall-of-text, but nearly every clause conveys actionable 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?

For an 8-parameter, unannotated tool with an output schema (so return values need not be explained), the description covers usage, sizing, pagination, on-demand registration, and expiry behavior. Nothing essential to correct invocation appears missing.

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

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

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning beyond it: on-demand registration semantics for chain+contract, case-sensitivity of Solana mints, 'use alone' for candidate, and the pagination relationship between after and next_after. It enriches rather than merely restates 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?

States a specific verb+resource ('Catalog lookup') and the concrete outcome ('turn tickers or names into uids'). This clearly separates it from the get_* siblings, which retrieve other data types. An agent can identify the tool's job without opening the schema.

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?

Explains the core use case (tickers/names -> uids) and gives per-filter selection guidance ('use alone' for candidate, 'chain+contract' for registering unknown mints). It does not explicitly name sibling tools or state when not to use it, so it stops short of a 5.

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