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TypeScript

Hybrid

Get Post-Offering Price Performance

get_dilution_performance

Read-only

Assess how the market absorbed past stock offerings by retrieving post-pricing price performance for a ticker. Returns historical measured results, clearly flagged as not a forecast and never implying future expectations.

Instructions

Get post-offering price performance (10 credits): for each recorded offering, how the stock traded after it priced or closed. Use when assessing how the market has absorbed this issuer's past financings. 🔴 THIS IS HISTORY, NOT A FORECAST — it describes what happened after prior offerings and must never be phrased as an expectation, a price target or a prediction about a named company. An empty or unavailable series means no offering performance was measured, NOT that offerings had no effect and NOT that there were no offerings. ALWAYS RETURNS 200 — read available and reason, never the HTTP status. Coverage is partial: not_covered means THIS COMPANY HAS NOT BEEN ANALYSED YET, which is different from it having no dilution and different from an outage (an outage is a 5xx). Never report a company as having no dilution on the strength of an unavailable response. NULL, absent or withheld means NOT MEASURED — it NEVER means "no dilution", "no warrants", "no shelf" or zero. A numeric 0 is a MEASURED zero and defeats a null check, so decide from the accompanying availability/withheld/reason field, never from the value itself. If a figure is absent, say it was not measured; do not describe the company as having none of that instrument. Figures are as of meta.asOfDate, from filings read through meta.filingsThrough — a stale as-of date means the answer predates anything filed since, not that nothing has happened. Share counts are rebased onto a single split basis; a row that cannot be rebased is omitted and counted rather than published on a mixed basis. Included in Signal8 Pro — without it the call returns 403 with code FEATURE_NOT_AVAILABLE and requiredTier: "pro". That is a subscription-tier upgrade, not an add-on purchase; there is no separate add-on to buy.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`ticker`	Yes	Stock ticker symbol (e.g., "MNTS", "VNRX")

Output Schema

TableJSON Schema

Name	Required	Description	Default
`data`	No

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.1.2Sep 23, 2026

TDQS

A4.2/5.0

Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses extensive behavioral nuances: the tool always returns 200 and requires reading `available` and `reason`; coverage is partial (`not_covered` vs no dilution vs outage); NULL/absent values mean 'not measured' never zero; numeric 0 is a measured zero; data is as-of a date with stale-date caveats; share counts are rebased with omitted rows; and a Pro-tier requirement returning 403. This goes far beyond the annotation and gives the agent critical interpretation guidance.

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

Conciseness2/5

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

The description is highly verbose and repetitive. For example, the warning 'NULL, absent or withheld means NOT MEASURED' appears in different phrasings multiple times, and the 'not_covered' explanation is also repeated. While the content is structured (purpose → usage → warnings), it does not earn every sentence's place; many warnings could be consolidated. The front-loading of the purpose is good, but the overall length is disproportionate for a single-parameter read-only tool.

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

Completeness5/5

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

Given the data's interpretive complexity (null vs zero, coverage distinctions, HTTP status behavior, staleness, share rebasing) and the presence of an output schema, the description covers every edge case an agent needs to avoid misreporting. It even addresses subscription-tier requirements and error codes. Nothing essential is missing for correct invocation and interpretation.

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

Parameters3/5

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

The only parameter is `ticker`, which is fully described in the schema ('Stock ticker symbol (e.g., "MNTS", "VNRX")'). The description adds no additional meaning about the parameter itself—no format validation, no examples beyond the schema, no mention of case sensitivity or exchange. Since schema coverage is 100%, the baseline of 3 applies, and the description does not elevate it.

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

Purpose5/5

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

The description opens with a specific verb-resource pair ('Get post-offering price performance') and explicitly states the data scope: 'for each recorded offering, how the stock traded after it priced or closed.' It also adds a clear use-case sentence. This distinguishes it from siblings like get_dilution_history or get_dilution_coverage, which focus on dilution amounts/coverage rather than post-offering price action.

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

Usage Guidelines4/5

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

The description provides a clear usage context: 'Use when assessing how the market has absorbed this issuer's past financings.' This tells the agent when to invoke the tool. However, it does not explicitly mention when not to use it or name alternative tools (e.g., get_dilution_risk for risk assessment), so it lacks the explicit exclusions/alternatives that would earn a 5.

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

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