forecast_epsRetrieve EPS forecasts and analyst estimate history for any US or HK stock symbol to assess market expectations and track estimate trends.
Get EPS forecast and analyst estimate history.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | Security symbol, e.g. "700.HK" |
| Name | Required | Description | Default |
|---|---|---|---|
| items | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.7.1Input schema / titleRemoved value: -"SymbolParam"v0.6.0Output schema / $defs / ForecastEpsItem / descriptionRemoved value: -"One record in `forecast_eps`'s `items`.\n\nSubset of documented fields; upstream may return more."Output schema / $defs / ForecastEpsItem / properties / analyst_count / descriptionRemoved value: -"Number of contributing analysts."Output schema / $defs / ForecastEpsItem / properties / eps_actual / descriptionRemoved value: -"Actual reported EPS."Output schema / $defs / ForecastEpsItem / properties / eps_estimate / descriptionRemoved value: -"Consensus EPS estimate."Output schema / $defs / ForecastEpsItem / properties / forecast_end_date / descriptionRemoved value: -"Forecast period end (RFC3339; rewritten from a unix-epoch field)."Output schema / $defs / ForecastEpsItem / properties / forecast_start_date / descriptionRemoved value: -"Forecast period start (RFC3339; rewritten from a unix-epoch field)."Output schema / $defs / ForecastEpsItem / properties / surprise_pct / descriptionRemoved value: -"Surprise percentage (actual vs estimate)."Output schema / $schemaRemoved value: -"https://json-schema.org/draft/2020-12/schema"Output schema / descriptionRemoved value: -"Returned by `forecast_eps`. Wraps an `items` array of EPS estimates.\n\nSubset of documented fields; upstream may return more."Output schema / properties / items / descriptionRemoved value: -"EPS forecast / actual records."Output schema / titleRemoved value: -"ForecastEpsResponse"v0.5.6Output schema / (root)Previous value: -nullNew value: +{
+ "$defs": {
+ "ForecastEpsItem": {
+ "description": "One record in `forecast_eps`'s `items`.\n\nSubset of documented fields; upstream may return more.",
+ "properties": {
+ "analyst_count": {
+ "description": "Number of contributing analysts.",
+ "format": "int64",
+ "type": [
+ "integer",
+ "null"
+ ]
+ },
+ "eps_actual": {
+ "description": "Actual reported EPS.",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "eps_estimate": {
+ "description": "Consensus EPS estimate.",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "forecast_end_date": {
+ "description": "Forecast period end (RFC3339; rewritten from a unix-epoch field).",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "forecast_start_date": {
+ "description": "Forecast period start (RFC3339; rewritten from a unix-epoch field).",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "surprise_pct": {
+ "description": "Surprise percentage (actual vs estimate).",
+ "type": [
+ "string",
+ "null"
+ ]
+ }
+ },
+ "type": "object"
+ }
+ },
+ "$schema": "https://json-schema.org/draft/2020-12/schema",
+ "description": "Returned by `forecast_eps`. Wraps an `items` array of EPS estimates.\n\nSubset of documented fields; upstream may return more.",
+ "properties": {
+ "items": {
+ "description": "EPS forecast / actual records.",
+ "items": {
+ "$ref": "#/$defs/ForecastEpsItem"
+ },
+ "type": [
+ "array",
+ "null"
+ ]
+ }
+ },
+ "title": "ForecastEpsResponse",
+ "type": "object"
+}v0.4.5v0.4.0v0.3.2v0.3.1v0.1.12Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The tool has strong annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint false) that disclose its safety and idempotency. The description adds no behavioral details beyond what annotations provide, such as data freshness or rate limits, but also does not contradict them. So a score of 3 is appropriate.
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 extremely concise—one sentence front-loaded with the core action. It is efficient but could include a bit more context without becoming verbose. There is 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?
The tool has an output schema (not shown), so the description does not need to explain return values. However, the description could provide more context about the data's temporal scope (e.g., current forecast vs. historical) or data source, which is missing. For a simple tool, it is barely 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?
The schema has 100% coverage for its single parameter 'symbol', already containing a description. The tool description does not add any extra semantic meaning or usage tips beyond what the schema provides, so it meets the baseline of 3.
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 'Get' and clearly states the resource 'EPS forecast and analyst estimate history', distinguishing it from sibling tools like 'consensus' which cover broader estimates. The purpose is immediately 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?
The description provides no guidance on when to use this tool versus alternatives, such as when to use 'consensus' for different analyst data. No prerequisites or exclusions are mentioned, leaving the agent to infer usage context.
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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