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Food log

get_food_log

Read-onlyIdempotent

Decrypt and read local daily food-intake logs, newest first, filtered by recent days or a date range for meal and nutrition review.

Instructions

Decrypt daily food-intake logs locally (newest first).

limit counts LOGGED days: by default the newest 14 days that have a log — not a calendar fortnight. Days with nothing logged are skipped, so the 14 can reach further back; coverage.span_days and coverage.days_missing_in_span say how far. since / until ("YYYY-MM-DD", local days, both inclusive) read a calendar window instead — "what did I eat in the first week of August" — and then limit applies only if you pass it. Each day's date is its local calendar day (the same as local_date, and the form log_food_entry and get_strength_log use). A logged day is roughly 0.5-1k tokens (more with notes, and Chinese text counts more), so a month can already be past what a client takes in one result — it then comes back as result_too_large; ask for fewer days or a narrower window.

Each day carries meals, each meal a list of items with food (name), optional free-text portion ("1 根" / "300g" / "小份"), optional per-item/per-meal note, and — when the logging agent estimated them — optional structured nutrition numbers (kcal, proteinGrams, fatGrams, carbGrams). Items without those fields need analysis-time estimation from name + portion; items with them can be summed directly. Owner's own days only. Pass limit_days to cap how many days return.

Carries a coverage block: quote coverage.days_covered (distinct days, not the row count) and coverage.span_days beside any average or trend. coverage.window_satisfied: false alone does NOT mean a short history: it is also false when older days exist beyond your window. 🔴 Before saying how far back someone's data goes, read coverage.more_available: true means this server can decrypt days OLDER than first_day that your limit left behind — re-read with a larger limit, or quote coverage.oldest_available as the real start of their history. Never report a limit-shaped window as the extent of their data.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`fresh`	No
`limit`	No
`since`	No
`until`	No
`limit_days`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed6 schema fields changedv0.9.1Oct 3, 2026

addedInput schema / additionalProperties
```
Added value: +false
```

addedInput schema / properties / limit / anyOf

Added value: +[
+  {
+    "type": "integer"
+  },
+  {
+    "type": "null"
+  }
+]

changedInput schema / properties / limit / default
```
Previous value: -90New value: +null
```
removedInput schema / properties / limit / type
```
Removed value: -"integer"
```

addedInput schema / properties / since

Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Since"
+}

addedInput schema / properties / until

Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Until"
+}

First observedv0.8.1Sep 23, 2026

TDQS

A4.3/5.0

Behavior5/5

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

Annotations already declare read-only/idempotent/non-destructive, but the description adds substantial behavior the annotations cannot: token cost per logged day, the `result_too_large` failure mode, filigree of the `coverage` block, and the critical `coverage.more_available` flag that warns against reporting a limit-shaped window as the true extent of history.

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

Conciseness4/5

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

Long but front-loaded and largely earned by the tool's complexity and the near-empty schema. Some redundancy remains (two separate day-capping params, repeated coverage guidance), so it is dense rather than wasteful.

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

Completeness4/5

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

Given an output schema exists, return-value explanation is not strictly required, yet the description usefully explains the coverage/meals/items shape and the failure mode. It is nearly complete for a complex 5-param read tool, short only on the undocumented `fresh` parameter.

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?

Schema coverage is 0% across 5 params, so the description must carry the load. It well explains `limit`, `since`/`until` (format and inclusivity), and touches `limit_days`, but `fresh` is never mentioned and `limit` vs `limit_days` is left ambiguous ('cap how many days return'), which is a real gap for a 0%-coverage schema.

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?

Opens with a specific verb+resource: 'Decrypt daily food-intake logs locally (newest first).' An agent immediately knows this is a read of logged meals, distinct from the sibling writers log_food_entry/log_food_append and from get_strength_log which it referencing-crosses by date format.

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?

Gives clear in-tool routing: use `limit` for 'newest N logged days' vs `since`/`until` for a calendar window ('what did I eat in the first week of August'), and tells the agent to ask for fewer days on `result_too_large`. It does not, however, compare this tool against any alternative sibling (e.g. get_notes), so no explicit when-not guidance.

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