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

With no annotations, the description carries the full burden and does well: it names the data source (Finnhub), discloses the fallback to SEC 8-K Item 2.02 estimation, and says estimates are marked estimated=True. It omits auth/rate-limit behavior, keeping it below 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?

Three short lines, front-loaded with the core action and limits, then source and caveats. Every sentence carries information; no padding.

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 details are not required; the description still notes that rows carry their own method/source and estimated flag, and explicitly excludes EPS/revenue. Adequate for a 2-param read tool.

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

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

Schema description coverage is 0%, so the description must compensate and does: it gives the days_ahead range (1-180) and a max of 20 tickers, neither present in the schema. It does not restate the default of 30, a minor gap.

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: 'next earnings date per ticker within days_ahead days', with scope limits (1-180 days, max 20 tickers). It is unambiguous, though no sibling is named because none of the listed tools cover earnings dates.

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 scope (days ahead, max 20 tickers) implies when the tool applies, but there is no explicit when-to-use/when-not-to-use guidance or pointer to an alternative for EPS/revenue data beyond the negative 'No EPS or revenue forecasts are returned.'

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 discloses chunk size (~24,000 characters), cursor-based continuation, plain-text return, and SEC EDGAR source. It omits rate limits, authentication needs, and error behavior, so it is useful but not exhaustive.

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 the core return, then pagination, dependency, and source in compact sentences. No wasted 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?

An output schema exists, so return shape need not be explained; the description covers chunking, pagination with cursor, and where to get accession numbers. It is complete enough for an agent to invoke the tool correctly.

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

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

Schema description coverage is 0%, so the description must compensate; it explains cursor as the next-chunk token, gives an exhibit example, and points to list_filings for accession numbers. Ticker and exact accession format are left to inference, so compensation is partial.

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 it returns plain text of one filing exhibit, gives the EX-99.1 press-release example, and scopes it to chunked retrieval. It distinguishes from list_filings by directing accession lookup there, but does not explicitly contrast with sibling get_filing_section.

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 the prerequisite to get accession numbers from list_filings and explains pagination by passing next_cursor. Gives clear context for use, but does not state when to prefer this over get_filing_section or when not to use it.

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 usefully discloses chunk size (~24,000 characters), cursor-based pagination, plain-text output, and SEC EDGAR as the source. It does not mention rate limits, authentication, or error behavior, but the core retrieval behavior is 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?

The description is front-loaded with the core operation and then efficiently covers section formats, pagination, and accession sourcing. Every sentence adds useful information without wasting space.

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 fully described, and the description still explains chunking and cursor behavior. It is nearly complete for invocation, though ticker semantics and any error/rate-limit context are absent.

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 0%, so the description must explain parameters, and it does so well for section, cursor, and accession. Ticker is left implicit as a company ticker, which is a minor gap.

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 and resource: retrieving the plain text of one 10-K or 10-Q section, returned in chunks. It does not explicitly distinguish this from sibling tools such as get_filing_exhibit, but the section-level scope is clear.

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

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

It gives clear operational context: get accession numbers from list_filings, use section identifiers such as "1A" or "Risk Factors," and pass next_cursor for subsequent chunks. It does not explicitly say when to choose this over get_filing_exhibit or when not to use it.

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

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

With no annotations provided, the description carries the burden and does so fairly well: it discloses the data source, newest-first ordering, and that missing values are null with a reason. It also describes per-value metadata such as XBRL tag, period dates, form, accession, filed date, and URL, though it does not mention rate limits or permission requirements.

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

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

The description is front-loaded with the core purpose, then structured into metrics, periods, n, return metadata, missing-value behavior, and source. Every sentence adds useful information with 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 the absence of annotations, an output schema, and four parameters, the description supplies enough context to call the tool correctly: it covers the data source, allowed metrics, period options, range for n, ordering, missing-value behavior, and per-value provenance. Return shape is handled by the output schema, so no further return-format explanation is needed.

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

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

Schema description coverage is 0%, so the description must compensate, and it largely does: it enumerates all valid metrics with the default of all, defines periods as annual or quarterly, and constrains n to 1-40. It omits the n default of 5 and does not explicitly define ticker, but those are minor gaps.

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 resource and source: company fundamentals from SEC XBRL filings (10-K/10-Q), newest period first. It is clearly distinct in subject matter from siblings like get_price_history or get_insider_transactions, though it does not explicitly name alternatives.

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 by the tool's domain and the detailed parameter values, but there is no explicit when-to-use guidance or comparison to siblings such as list_filings or get_filing_section. An agent can infer the tool is for financial fundamentals, but must decide on its own when this is preferable to filing-level 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?

With no annotations, the description carries the full burden and does so well: it discloses that filings where the company is an owner of another issuer are skipped, that Form 4/A amendments replace originals in totals, and that non-P/S codes are labelled not open-market. It omits auth/rate-limit and pagination behavior, but the substantive data-handling semantics are unusually 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?

Dense but front-loaded: the resource and ordering come first, then the counting rules, then amendments/skips, then parameter detail. Some clauses (derivative flag, Rule 10b5-1 flag) border on return-value enumeration better suited to the output schema, but nothing is pure 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?

An output schema exists, so explaining return values is largely redundant, yet the description still signals what each row contains. Combined with the data-exclusion semantics, an agent has what it needs to call correctly; only the `limit` semantics and any auth/rate constraints are 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 0%, so the description must compensate. It fully documents `since` (filing date, YYYY-MM-DD, default 12 months ago) but says nothing about `limit` (schema default 50) or clarifies `ticker` format — partial compensation only, so the 3 baseline is right.

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 (SEC Form 4 insider transactions), the returned fields, ordering ('newest first'), and the domain rules that distinguish it from sibling filing/fundamental tools. An agent can tell immediately this is the insider-trading dataset, not general filings or price 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?

Implicitly scopes usage through the `since` default (12 months) and the explicit rule that only codes P and S count as open-market. It doesn't name a sibling alternative or state when NOT to use it, so it falls short of a 5.

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

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

With no annotations, the description carries the behavioral disclosure burden and does well: it explains return fields, missing-value handling, observation ordering, limit/start filtering, as_of, the 10-series cap, and the FRED source. It does not mention permissions, rate limits, or error behavior, so it stops short of fully transparent.

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 purpose is front-loaded in the first sentence, followed by return semantics and constraints with no filler. Every sentence contributes useful operational 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 a three-parameter read tool with an output schema and no annotations, the description supplies enough operational context to invoke it correctly: it covers series examples, the cap per call, limit/start semantics, and return behavior.

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

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

Schema description coverage is 0%, so the description must compensate. It explains limit ('last limit points'), start ('YYYY-MM-DD'), and series IDs through examples plus the 'Up to 10 series per call' constraint, though it does not explicitly restate that series_ids is a required array.

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 and resource: 'Fetch FRED macro time series' with concrete series examples. The resource is clearly distinct from the sibling tools, which cover prices, filings, fundamentals, and earnings rather than macro time series.

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 by the domain-specific purpose and examples such as DGS10 and CPIAUCSL, but the description does not explicitly say when to use this tool versus alternatives or when not to use it. No sibling routing guidance is provided.

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

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

With no annotations, the description carries the burden and does disclose real behavior: single-provider sourcing with no fallback, the response naming the provider, hard truncation to the `limit` most recent bars, and daily-only granularity. It omits error behavior for invalid tickers, timezone/session semantics, and whether missing bars are dropped or padded.

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 short sentences, front-loaded with what the tool returns before the constraints. No filler, though the trailing clause about provider fallback could be folded more tightly into the first sentence.

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 5-parameter tool with 0% schema description coverage, no annotations, and only an output schema to lean on, the description covers the essentials but leaves default/null resolution for start and end, limit bounds, and range-inversion behavior undocumented. It is callable but not fully specified.

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 0%, so the description must compensate. It adds meaning for four of five params: date format for start/end, '1d' only for interval, 'at most `limit` most recent bars' for limit, and US-only scope for ticker. It never explains what null start/end defaults to, whether limit has a maximum, or the ordering of returned bars.

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+shape: 'Daily price bars (OHLCV plus adjusted close) for a US ticker from one price provider.' That is far more precise than the name alone and scopes it to US tickers and daily granularity. It never names the nearest sibling (get_price_stats), so the differentiation is only implied.

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

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

It rules out things ('Only interval "1d" is supported', 'no fallback to another one'), which usefully prevents wrong calls. But it gives no positive guidance on when to pick this over get_price_stats or get_macro_series, and no prerequisites such as whether the ticker must be resolved first via resolve_ticker.

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 disclose meaningful behavior: adjusted closes, exact trading-day windows, max drawdown peak/trough dates, and annualized volatility. However, it leaves out important context such as which provider is used, whether ticker resolution is required, and whether adjusted-close data may differ from other price tools.

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 dense sentence that front-loads the resource and lists the specific computed metrics without filler. Every clause adds useful information about what is returned or how it should be interpreted.

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 the description need not fully explain return values, and it already adds useful metric detail. Still, it lacks sibling differentiation and ticker semantics, which are relevant gaps for correct tool selection and 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?

The schema has zero description coverage and the description never mentions the ticker parameter or its expected format. For a single required parameter named ticker, the meaning is somewhat obvious, but exchange, symbol format, and resolution behavior are undocumented.

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 and scope: price statistics computed from one provider's adjusted closes, with returns, drawdown, 52-week range, and volatility. It is clear what the tool returns, but it does not explicitly distinguish itself from the sibling get_price_history tool.

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 says the output is descriptive only and not a recommendation, which gives some context, but it provides no explicit guidance on when to use this instead of get_price_history or other price-related siblings. The agent must infer the choice from metric names alone.

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

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

No annotations are provided, so the description carries the behavioral burden. It usefully discloses result ordering ('newest first') and the data source (SEC EDGAR), but says nothing about pagination, EDGAR rate limits, or error behavior for unknown tickers — notable gaps for an external-API-backed read tool.

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

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

Three short sentences, front-loaded with purpose, then filters, then provenance. Every sentence carries information and nothing is redundant.

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 shape needn't be described, and the read-only nature is self-evident from 'List'. The description is nearly sufficient, with the only real shortfall being `limit`/pagination semantics for a tool that can return large filing histories.

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 0%, so the description must compensate; it does add real value for two of four params — an example array for `forms` and the YYYY-MM-DD format for `since` (which the bare string schema lacks). However, `limit` (default 20, max unknown) and `ticker` format go unexplained.

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 ('List a company's SEC filings') plus the ordering ('newest first'), which cleanly separates it from siblings like get_filing_section and get_filing_exhibit that fetch document contents rather than the filing 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 gives context (company filing index, SEC EDGAR source) and mentions the two filters, so the intended usage is implied, but it never states when to prefer this over get_filing_section/get_filing_exhibit or what to do with the results. No exclusions or alternatives are named.

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 bears full disclosure burden; it conveys that this is a non-mutating liveness check by implication, but never states side-effect-free behavior, cost/latency, or what a failure looks like. Because an output schema exists, omission of return details is acceptable, keeping this at a minimum-viable 3.

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 front-loaded sentence with no filler, exactly proportionate to a trivial liveness probe. Every word earns its place.

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

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

For a zero-parameter health check with an output schema already defining the response, the description covers what an agent needs to select and invoke it. A brief note on typical placement (pre-flight vs. troubleshooting) would close the only remaining gap.

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 baseline for a parameterless tool applies. The description adds no misleading parameter hints, which is the correct behavior here.

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 ('check') and resource ('the market-research server is running'), making the health-check nature unambiguous. It does not explicitly differentiate itself from the ten data-retrieval siblings, but the distinction is obvious from the verb and the zero-parameter 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?

Usage is only implied: an agent can infer this is a connectivity/liveness probe, but the description never says when to call it (e.g., before a batch of data calls, or after a suspected outage) or that it should not be used to validate data availability. No alternatives or exclusions are named.

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 behavioral burden. It adds useful context by naming the source, SEC EDGAR, and listing the returned identifiers, but it does not describe behavior for ambiguous matches, not-found cases, rate limits, or authentication requirements.

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 compact sentences with no wasted text. The core action and output fields are front-loaded, followed by the data source.

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 tool is simple, has one parameter, and an output schema exists, so the description does not need to explain return values in detail. It adequately covers the input and output identifiers, though it omits edge-case behavior for ambiguous or invalid queries.

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

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

Schema description coverage is 0%, so the description must clarify the single parameter, and it does: 'query' accepts either a ticker (with examples MSFT, BRK.B) or a company name. This meaningfully compensates for the bare schema, though it does not specify case sensitivity or ambiguity handling.

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, 'Resolve', and clearly states the resource: a ticker or company name mapped to SEC CIK, exchange, and SIC industry code. It is precise enough that an agent can distinguish this lookup tool from sibling tools like get_price_history or list_filings.

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

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

The description makes the usage context clear: it is for converting a ticker or company name into SEC identifiers. It does not explicitly state when not to use it or name alternatives, but no sibling tool provides the same resolution capability, so the context is sufficient.

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