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pond

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"I know we discussed that before. Why can't I find that damn conversation?"

Pond makes every AI agent session you've ever run - Claude Code, Codex, any tool, any machine - searchable in one place.

Your agent history is already on your disk: thousands of sessions full of decisions, fixes, and dead ends - scattered across tools that can't search them. Pond ingests them all automatically and losslessly into storage you own (a local dir or your own S3 bucket), makes the whole corpus searchable and SQL-queryable, and hands that recall back to your agents over MCP - so "how did we fix this before?" is a query, not an archaeology dig. Sessions stop being locked to the tool that created them: any session can be restored into any supported client and continued there.

brew install tenequm/tap/pond   # macOS / Linux

scoop bucket add tenequm https://github.com/tenequm/scoop-bucket   # Windows
scoop install tenequm/pond

Or prompt your agent: "Please install and set up pond (see github.com/tenequm/pond)" - the full, failure-proofed version of that prompt is in Connect your agents.

Status: pre-v1. Schemas, wire shapes, and config keys are subject to breaking change until v1. Full documentation lives at pond.locker; the contract is docs/spec.md.

Quickstart

Install, run guided setup, and ingest your local sessions:

brew install tenequm/tap/pond   # macOS / Linux; Windows: Scoop or a release zip - see Install below
pond init   # guided setup: storage, adapters, MCP + agent skill, optional schedule
pond sync   # ingest and index - every enabled adapter

pond init registers pond as an MCP server for Claude Code and installs the bundled pond skill; for Codex it prints the command to run instead - restart the client afterwards so the tools load. By hand: claude mcp add -s user pond -- pond mcp, codex mcp add pond -- pond mcp; skill: mkdir -p ~/.claude/skills/pond && pond skill > ~/.claude/skills/pond/SKILL.md (that whole line is POSIX-only - mkdir -p, &&, and > all break or corrupt in Windows PowerShell 5.1; use the PowerShell block in Connect your agents). Then ask your agent - real prompts from daily use:

check in pond how we solved this before, then apply the same fix here
where we left off yesterday - check pond, then continue
are you sure that won't break X? check in pond how we struggled with exactly this

Sessions are picked up automatically from Claude Code, the Claude desktop app (local agent mode), Codex CLI, opencode, pi-coding-agent, oh-my-pi, OpenClaw, NanoClaw, Hermes Agent, letta-code, and grok-build. A Claude.ai data export imports with pond sync claude-ai-export --path <path> (manual download, so not auto-discovered).

Related MCP server: suasor

Isn't this another memory tool?

No - it's the layer underneath one. Memory tools store what they decided you'd need - facts, summaries, filed chunks; the sessions themselves are gone. Pond keeps the sessions: every message, tool call, and result, value-complete, cross-client, in storage you own, never pruned - searchable over MCP and restorable into any client. Memory is a derived view you can always rebuild from an archive; an archive can never be rebuilt from memories.

Three kinds of tool get called "memory". Side by side:

pond

Session search (ctx, deja-vu, cass)

Memory layers (Mem0, Letta)

History from before install

yes

yes

no

What is kept

the whole session

a search index ¹

extracted facts

Where it lives

local dir or S3 bucket

local index

the tool's database

After the harness deletes the file

still there

gone at next refresh ¹

only the extract

Several machines

one shared bucket

pulled into one machine ²

shared server or cloud

Agent access

CLI, MCP, HTTP, SQL

CLI, MCP ³

HTTP, SDK, MCP

¹ cass also mirrors the raw files, so they outlive the source. ² deja-vu copies records between machines over ssh; cass pulls other hosts' session files over ssh/rsync into its local index; ctx is single-machine. ³ cass has no MCP server.

Pick session search for fast local recall. Pick a memory layer when the agent should carry distilled facts, not the record. Pick pond when you want the sessions themselves, in storage you own, from every machine you run.

Full comparison, with receipts per tool: pond.locker/compare. Every cell is a claim about a specific version of someone else's project. If one has gone stale, open an issue and it gets fixed the same day.

Background

Every agentic CLI ships its own session format and its own search surface. Switching tools means losing history. Replaying a Claude Code session in another provider's tooling means re-translating the wire shape by hand. Hosted multi-tenant deployments rebuild the same storage layer from scratch.

Pond is the storage and retrieval layer that sits underneath. Every adapter is a bidirectional codec between a client format and one canonical schema, so any session can be restored by any adapter - it need not return to the client that produced it. Storage, search (BM25 full-text by default, with optional semantic search, one arm per query), and provider-agnostic replay all sit on a single Lance-on-object-storage foundation.

The v1 surface includes: full CLI, HTTP+JSON and MCP transports, search over three Lance datasets, opt-in intfloat/multilingual-e5-small embeddings at FP16 weights (Metal on macOS, CUDA opt-in, CPU fallback), and local-FS / S3 / GCS / Azure backends through Lance's object_store integration.

Install

Linux, macOS, and Windows are supported.

macOS and Linux:

brew install tenequm/tap/pond              # Homebrew
nix profile add github:tenequm/pond#pond   # Nix

Windows (Scoop, the primary channel - it also ships pondw.exe, the windowless launcher that scheduled sync runs through):

scoop bucket add tenequm https://github.com/tenequm/scoop-bucket
scoop install tenequm/pond

Buckets are git clones, so the first line needs git on PATH - if it fails with "Git is required for buckets", run scoop install git and retry.

No Scoop yet? Bootstrap it first, from a normal (non-admin) PowerShell (its installer refuses an elevated shell):

Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser -Force
irm get.scoop.sh | iex

Then open a new terminal - PATH changes reach only processes started after the install. See the Windows notes for Defender, long paths, scheduling, and WSL.

No package manager (any platform): every release attaches prebuilt binaries (pond-x86_64-pc-windows-msvc.zip on Windows, ~223 MB unpacked) - unpack one and add its directory to PATH (on Windows: Settings > System > About > Advanced system settings > Environment Variables, under your user variables), nothing else to install.

Via cargo (any platform, needs the Rust toolchain):

cargo binstall pond-db   # downloads the prebuilt binary (needs cargo-binstall)
cargo install pond-db    # builds from crates.io (installs the `pond` command)

Both install pond.exe only, so scheduled sync on Windows - which runs through pondw.exe - wants the Scoop or zip install instead (or --features windows-launcher on a source build). cargo install also needs the protoc and NASM prerequisites below.

Build from source:

git clone https://github.com/tenequm/pond.git
cd pond
cargo install --path packages/pond

On Windows that clone needs git config --global core.longpaths true first (test-fixture paths exceed 260 characters), and the build needs an explicit --target x86_64-pc-windows-msvc - without it cargo applies the repo's +crt-static flag to build scripts and proc-macros too, which then fail to load.

For CUDA acceleration on Linux:

cargo install --path packages/pond --features cuda

On macOS the Metal backend is selected automatically; on other systems the CPU fallback runs without extra features. Building from source on Windows additionally needs protoc (winget install Google.Protobuf) and NASM (winget install NASM.NASM, then add C:\Program Files\NASM to PATH yourself - its installer doesn't) on PATH.

If any install path fails, the Troubleshooting guide covers the common stalls.

Usage

Set up storage, adapters, MCP registration, and an optional sync schedule in one pass (idempotent - re-run it any time to repair or update):

pond init

Then import sessions from local adapters, update indexes, and search:

pond sync
pond search "how did we wire up the OCC retry loop"

Run a server

pond serve                         # HTTP on 127.0.0.1:9797
pond serve --transport stdio       # MCP over stdio
pond mcp                           # alias for stdio MCP

Fetch and copy

Fetch a single session or message, or move a whole corpus:

pond get-session <id>
pond get-message <id>
pond copy --from local --to snapshot.pond
pond copy --from snapshot.pond --to local

Read-only SQL

Ask structured questions with read-only SQL (the same surface as the pond_sql MCP tool):

pond sql "SELECT project, count(*) FROM messages GROUP BY project ORDER BY 2 DESC"

Maintenance

Run maintenance on demand (sync folds indexes on every run):

pond optimize --only embed   # only when [embeddings].enabled = true
pond optimize --only index

Scheduled sync

Keep pond current automatically (launchd on macOS, systemd user timers or cron on Linux, Task Scheduler on Windows - there, run it from a normal shell, not an elevated one, or the task ends up owned by Administrators):

pond schedule start                # every 5m by default (--every 15m|1h|6h|1d)
pond schedule status
pond schedule logs

Status and introspection

pond status prints a per-table storage table, then indexes (text readiness, plus the semantic half only when embeddings are enabled), stored (sessions + messages), agents (source agents in the store), and this host's view of it: per-adapter sessions pending sync, the last sync's outcome (including a surfaced failure from a scheduled run), and the next scheduled run. pond status --hosts breaks a shared store down by ingest host; --include-subagents counts each subagent as its own agent. pond sync --dry-run previews what the next sync would read. pond search --explain returns Lance's analyze_plan output for each retrieval arm.

Remote storage

By default pond stores data locally under ~/.local/share/pond (%LOCALAPPDATA%\pond\data on Windows). To use an object store, add credentials and switch the destination:

pond creds add                                                    # interactive: name, access key, hidden secret
pond storage use s3+https://nbg1.your-objectstorage.com/my-pond   # probe end-to-end, then flip [storage].path
pond storage check                                                # verify: parse, creds, conditional-put (OCC), write/read/delete

pond init --storage-path <url> configures a remote destination during setup and prompts for credentials inline when the destination is remote, so a bucket is one command. The s3+https://host/bucket form works for any S3-compatible store (Hetzner, R2, B2, MinIO); s3://, gs://, and az:// use the standard cloud SDK credential chain when no [creds.*] set matches. pond copy --from <local> --to <url> carries existing local data into the bucket - idempotent, never deletes the source, and on completion it rebuilds the destination indexes and verifies every row landed (exit 6 if any are missing or duplicated, so you never reconcile by hand). pond copy --verify-only --from <local> --to <url> runs that same check read-only, without copying. Full walkthrough: pond.locker.

Configuration

pond init walks through everything below interactively and enables the adapters it finds. pond sync only ingests already-enabled adapters - enabling one is an explicit step (pond adapters enable / pond adapters discover / pond init), never a side effect of sync. Config lives at ~/.config/pond/config.toml on macOS and Linux, %APPDATA%\pond\config.toml on Windows (pond config path prints it). Every [adapters.<name>] block needs enabled = true to be active; sections without it (or with enabled = false) are skipped. ~ in paths expands on every platform (%USERPROFILE% on Windows).

[adapters.claude-code]
enabled = true
path = "~/.claude/projects"

[adapters.codex-cli]
enabled = false                    # kept in config, skipped on `pond sync`
path = "~/.codex/sessions"

Search is BM25 full-text by default. Semantic search is opt-in and off unless you ask for it: with it off no pond process downloads or loads an embedding model, new messages get no vectors, and --mode vector is refused. Turn it on in config or with POND_EMBEDDINGS_ENABLED=true, then run pond optimize --only embed once to fill the backlog:

[embeddings]
enabled = true

Full detail, including what it costs and how mixed fleets behave, is in the configuration reference.

Supported harnesses

One adapter per harness, in pond adapters discovery order; Reads is the path pond init discovers and writes to [adapters.<name>].path - shown POSIX-style, with the same home-relative layout on Windows (~/.claude/projects is %USERPROFILE%\.claude\projects). Last verified is the most recent capture or refresh date of the adapter's committed fixture (packages/pond/tests/fixtures/adapter/), the corpus its mapping is tested against. Adapters are maintained best-effort, and format drift is safe by design: unknown record shapes still ingest losslessly, malformed input surfaces as a typed error naming the file. Adding a harness is routine work - see Contributing.

Adapter

Reads

Last verified

claude-code

Claude Code CLI, ~/.claude/projects

2026-08-14

claude-desktop-app

Claude Desktop / Cowork local agent sessions

2026-05-13

claude-ai-export

claude.ai data-export archive (manual --path)

2026-06-04

codex-cli

OpenAI Codex CLI, ~/.codex/sessions

2026-09-01

opencode

opencode, ~/.local/share/opencode (SQLite DB + legacy tree)

2026-07-14

openclaw

openclaw, ~/.openclaw

2026-05-13

nanoclaw

nanoclaw, ~/nanoclaw (the install root holding data/v2-sessions)

2026-05-14

hermes

Hermes Agent, ~/.hermes (state.db per profile)

2026-07-23

pi-coding-agent

pi, ~/.pi/agent/sessions

2026-08-06

oh-my-pi

oh-my-pi, ~/.omp/agent/sessions (ingest-only)

2026-08-14

letta-code

letta-code, ~/.letta/transcripts (a root relocated via LETTA_TRANSCRIPT_ROOT is configured as an explicit path)

2026-08-24

grok-build

grok-build (xAI grok CLI), ~/.grok/sessions (a relocated GROK_HOME is configured as an explicit path)

2026-08-24

Verbosity

Root-level -v / -vv / -vvv raise the tracing level (info / debug / trace); -q / -qq lower it. The default surfaces warnings only. RUST_LOG overrides the CLI flag when set; POND_LOG is no longer honored.

Design

The full contract is in docs/spec.md. Key choices:

  • Lance direct, no wrapper. The lance-format/lance crates are the only storage and search engine. No lancedb, no parallel abstraction. Storage, indexing, OCC, schema evolution, blob columns, versioning, and time-travel are all Lance. The read-only pond sql surface is DataFusion planning over the same Lance datasets - a query escape hatch, not a second engine.

  • Canonical Session / Message / Part interlingua. Owned in pond, in the shape of Effect v4's Prompt-side Part union. This schema is pond's product; everything else is machinery around it.

  • Three Lance datasets (sessions, messages, parts). messages carries the nullable embedding (vector + embedding_model) alongside denormalized filter columns (source_agent / project / role / timestamp) for single-stage filter pushdown.

  • No-synthesis adapter seam. Adapters parse source records through extractor helpers that make "invent a value" a compile error - model-no-synthesis, model-schema-honesty, and adapter-provenance-required are structural, not review rules.

  • Index lifecycle decoupled from writes. Writes commit data (including embeddings, computed inline at ingest when embeddings are enabled) without folding the search indexes. pond sync runs index maintenance by default, and pond optimize --only index runs it on demand; Lance merges index results with a flat scan over unindexed fragments, so reads stay correct.

  • Single-arm retrieval. Each query runs one retriever - fts (BM25, the default) or vector (cosine, with a gentle recency tiebreaker, offered only when embeddings are enabled) - chosen per query; no server-side fusion. --sort-by recency returns newest-first. Results group to one summary per session, keyed on session_root.

  • Language-neutral full-text. Word-level simple tokenizer with English stemming (ascii-folding on); tokens the stemmer does not recognize pass through unchanged and stay exact-matchable, so pond indexes sessions in any language alike.

  • Two transports, one handler set. HTTP+JSON (axum) and MCP (rmcp) both dispatch into the same handlers. Wire ops: pond_search, pond_get_session, pond_get_message, pond_ingest. MCP additionally exposes the read-only pond_sql tool and the schema://pond, schema://pond-sql, and stats://pond resources.

  • Opaque-string multi-tenancy. Each tenant is a namespace string the integrator supplies; pond does not authenticate, authorize, or model identity. The object store's IAM is the storage boundary.

  • Encryption is operational. Bucket SSE plus filesystem encryption; pond holds no keys and adds no application-level crypto.

Roadmap

pond ships in small steps. This table lists the steps in order. Done steps stay in the table. The roadmap board holds the same items with their issues. React or comment on an issue to influence the order.

#

Step

Status

1

Lossless ingest from Claude Code and Codex into Lance, local or S3

v0.5

2

Single-arm search: BM25 or vector, one arm per query

v0.10

3

Remote sync in under a minute; warm search in under a second

v0.11-v0.12

4

Tool-call columns and read-only SQL over the corpus

v0.13

5

Crash-safe local stores that self-heal on open

v0.14.0

6

Eleven harnesses, pond resume into any client, MCP registry, Windows

v0.14.11

7

BM25 becomes the default arm. Embeddings become opt-in. #164

v0.15.0

8

New adapters become routine: add-adapter playbook + conformance harness #172, letta-code #170, grok CLI #171 - twelve harnesses

v0.15.1

9

Lance 10, then 11: count pushdown, date-filter zonemaps, stemmer self-heal. #145

v0.16.0-v0.17.0

10

pond erase: the one sanctioned deletion. #45

⏭ Next

11

Remote reads as fast as local reads. #165

⏳ Later

12

Namespaces: keep work and personal sessions apart. #166

🔧 In progress (design)

13

Redaction on copy, export, and resume. Never at ingest. #167

⏳ Later

14

herdr plugin: every session in one list, live or not, on any machine. Resume, fork, hand off. #219

⏳ Later

The canonical model as a published contract: ATIF export and import, a /format page, a stability promise. Vote-gated, outside the numbered order. #180

🔧 In progress

Community adapters: ten harnesses wanted, playbook-driven - Antigravity CLI, Qwen Code, Crush, Cline, OpenHands, Copilot CLI, Cursor CLI, Droid, Kiro CLI, Roo/Zoo Code. adapter issues

🙋 Wanted

A capture daemon. pond reads what your harness already writes.

❌ Not planned

A hosted service that owns your bucket. Your storage stays yours.

❌ Not planned

Summaries or pruning of stored sessions. pond keeps the sessions.

❌ Not planned

Step 7 was based on data. Over 63 days, agents ran 1,126 searches against this archive. BM25 found the answer 61% of the time. Vector found it 37% of the time. Read the measurement.

This is a direction, not a contract. The order changes when the data changes.

References

The upstream schemas that shaped pond's canonical model are documented in docs/references/ (source URLs + why each matters; the vendored code itself is not redistributed). Real session captures live under packages/pond/tests/fixtures/adapter/.

Source

Why it matters

Effect-TS/effect

Effect v4 Prompt/Response Part unions. Pond's canonical types copy this shape.

sst/opencode

Effect Schema canonical Part union; SDK types; storage schema.

kilo-org/kilocode

OpenCode fork. Adds editorContext, plan-followup, kilocode-specific events.

badlogic/pi-mono

pi-coding-agent leaf-cursor branching and cross-provider conformance test matrix.

open-telemetry/semantic-conventions-genai

GenAI semantic conventions. Inspiration for shape overlap; pond does not derive from OTel.

packages/pond/tests/fixtures/adapter/

Session samples for thirteen source harnesses (claude_ai_export, claude_code, claude_desktop_app, claude_managed_agents, codex_cli, grok-build, hermes, letta-code, nanoclaw, oh-my-pi, openclaw, opencode, pi-coding-agent; real captures except the synthetic hermes state.db and the generated oh-my-pi corpus). Drives adapter design and serves as adapter test fixtures.

Contributing

Issues and pull requests are welcome. The most useful contributions right now:

  • An adapter for a harness pond does not read yet. The playbook is the add-adapter skill; the PR expectations are in CONTRIBUTING.md.

  • Spec feedback on docs/spec.md.

  • Pointers to additional reference schemas or session samples worth documenting under docs/references/.

  • Bug reports against the v1 surface (CLI verbs, wire ops, schema mismatches, OCC behavior, object-store backends).

For something bigger, the roadmap is the list. Comment on the issue before you start, so we agree on the scope. For other larger changes, open an issue first to discuss the direction. For security issues, see SECURITY.md.

Questions or feedback? Start a GitHub Discussion, or DM me on Telegram or X - I answer personally.

License

Apache-2.0 (c) 2026 tenequm

Available Tools

4 tools
pond_get_messageA
Read-onlyIdempotent

Expand one message with its full part bodies (tool_call / tool_result / reasoning / file), plus conversational neighbors for context. Pass a message_id from pond_search or a transcript line; context_before / context_after size the neighbor window (default 3, like grep -B/-A). For the whole session use pond_get_session. Response format details: resource schema://pond.

ParametersJSON Schema
NameRequiredDescriptionDefault
idYesThe message to expand - a message_id from pond_search or a transcript line.
context_afterNoConversational sibling messages to include after the target (mirrors grep -A). Default 3.
context_beforeNoConversational sibling messages to include before the target (mirrors grep -B). Default 3.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare the tool read-only and idempotent. The description adds meaningful behavioral context by explaining the output includes full part bodies and that context_before/context_after control neighbor inclusion (default 3, like grep -B/-A). No contradiction is present.

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

Conciseness5/5

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

The description is three sentences, front-loaded with the primary action, and every sentence earns its place. It packs core semantics, usage guidance, and a sibling pointer without waste.

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?

For a tool with only 3 parameters, no output schema, and strong annotations, the description covers what the tool does, how to identify the target message, the behavior of optional parameters, and a pointer to the alternative for broader context. It also points to a resource schema for response format details, making it complete.

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

Parameters4/5

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

Schema coverage is 100%, but the description enriches parameter meaning by explaining id can come from pond_search or transcript, and by giving the grep analogy for context_before/context_after defaults. This adds value beyond the bare schema descriptions.

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 uses a specific verb ('Expand') with a clear resource ('one message') and details what expansion includes (full part bodies: tool_call/tool_result/reasoning/file) plus conversational neighbors. It also distinguishes from sibling tools by explicitly pointing to pond_get_session for whole-session retrieval.

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 clearly instructs how to obtain the message_id (from pond_search or a transcript line) and names an alternative tool for whole-session needs ('For the whole session use pond_get_session'). It doesn't explicitly state when not to use it, but the context is unambiguous enough.

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

pond_get_sessionA
Read-onlyIdempotent

Read a whole past session as a chronological transcript - the tool for analyzing, reviewing, or summarizing a session (user/assistant text plus one-line tool/file refs; tool bodies stay one pond_get_message away). Pass the id from pond_search or a subagent footer; a message_id also works - it resolves to its parent session with the page anchored at that message. Paging: limit (default 20), from="end" reads the most recent turns first (the session's final state; late conclusions supersede early ones), after_message_id / before_message_id continue from a page marker. The first page lists subagent sessions in a footer - pass a listed id back to open one. Not for bulk export - use pond copy --to <file>. Response format details: resource schema://pond.

ParametersJSON Schema
NameRequiredDescriptionDefault
idYesThe session to read - a session_id from pond_search or a subagent footer. A message_id also works: it resolves to its parent session with the page anchored at that message.
fromNoWhich end to read the first page from: "start" (oldest, default) or "end" (most recent - the session's final state, e.g. to recover context after compaction). Pages stay chronological.
limitNoMax messages per page. Default 20, max 1000.
after_message_idNoPage forward: a message id from a prior page's bottom marker; returns messages after it.
before_message_idNoPage backward: a message id from a prior page's top marker; returns messages before it.

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint and idempotentHint, but the description adds substantial behavioral context: chronological ordering, paging semantics, from="end" reading final state, subagent footer listing, and that message_id resolves to parent session. It also warns about late conclusions superseding early ones.

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

Conciseness5/5

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

The description is dense but every sentence serves a purpose: purpose, id resolution, paging, subagent navigation, and bulk-export exclusion. It is front-loaded with the core purpose and avoids filler.

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?

For a tool with no output schema and nontrivial paging, the description covers the key aspects: content of the transcript, paging methods, id sources, and subagent handling. It points to resource schema://pond for return format, but does not detail response fields. Slight gap, but still robust enough for an agent to select and invoke correctly.

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

Parameters5/5

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

Though schema coverage is 100%, the description significantly enriches parameter meaning: it explains that message_id resolves to parent session, from="end" reads most recent turns first, and describes how after_message_id/before_message_id act as page markers. This goes far beyond the schema's basic field definitions.

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 and resource: "Read a whole past session as a chronological transcript." It clearly distinguishes from siblings (pond_get_message, pond_search, pond_sql) by noting tool bodies are one message away and that bulk export should use `pond copy`.

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

Usage Guidelines5/5

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

Explicitly frames when to use the tool: "the tool for analyzing, reviewing, or summarizing a session," and gives a clear alternative for bulk export. Also explains how to obtain the id from pond_search or a subagent footer, and advises against bulk export, directing to `pond copy`.

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

pond_sqlA
Read-onlyIdempotent

Advanced escape hatch: run ONE read-only SQL statement (SELECT/WITH, DataFusion / PostgreSQL-compatible) over the sessions / messages / parts tables. NOT for finding or reading conversations - pond_search and pond_get_session / pond_get_message cover almost all recall. Reach for SQL only for: aggregation (counts, group-by, joins, time buckets), exact strings or identifiers in conversational text (contains_tokens / fts), tool-call analytics and tool bodies, subagent sessions, bulk export (format=parquet|ndjson). Read resource schema://pond-sql FIRST - exact columns, indexed predicates, JSON access rules, worked examples; do not guess column names or JSON paths. Inline text output is row-capped and long cells clip with a +N chars marker (full values via format=parquet|ndjson); queries are wall-clock-capped (raise via timeout_seconds).

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesOne read-only SQL statement (SELECT/WITH only; writes rejected). Exact columns - messages(session_id, message_id, timestamp, role, source_agent, project, content [system-role only], search_text [the conversational text], embedding_model, options) | sessions(session_id, parent_session_id, parent_message_id, source_agent, created_at, project, options) | parts(session_id, message_id, id, ordinal, type, provenance, tool_name, call_id, is_failure, variant_data, options). parts.type enums use underscores: 'tool_call', 'tool_result', 'text', 'reasoning', 'file'. Tool bodies live in JSONB variant_data - tool_call is {call_id, name, params} (a Bash command is json_extract(variant_data, '$.params.command')), tool_result is {call_id, name, is_failure, result}; never CAST JSON columns. No substring index covers tool bodies: an unscoped LIKE over variant_data full-scans and times out on a remote store - scope-then-scan instead (collect session_ids WHERE contains_tokens(search_text, '...'), then match variant_data fields only within them; worked example in schema://pond-sql). Tool analytics: prefer the narrow native columns (tool_name, call_id, is_failure). Text search: WHERE contains_tokens(search_text, 'words'), or FROM fts('messages', '{...}') for BM25 ranking. Joins, indexed columns, JSON functions, pagination, worked examples: resource schema://pond-sql.
formatNoOutput format: "text" (default; rendered ASCII table with metrics footer, row-capped), "parquet", or "ndjson". For parquet/ndjson the full result set is written to a file and a `pond-sql-export://` resource link is returned (no truncation) - ndjson is the path for machine-readable JSON output.
timeout_secondsNoPer-query timeout in seconds (default 30, max 600). Raise it for a genuinely long-running query (e.g. a large remote-store scan); prefer narrower predicates and the indexed/native columns first.

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint), the description discloses critical runtime behaviors: output is 'row-capped' with 'long cells clip with a +N chars marker', queries are 'wall-clock-capped', and writes are rejected (in schema description). It also warns about performance pitfalls ('unscoped LIKE over variant_data full-scans and times out') and tells users to scope-then-scan. This goes well beyond annotation hints and prepares the agent for real-world 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.

Conciseness5/5

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

The description is a single, dense paragraph that front-loads purpose and exclusions, then lists use cases, then gives operational guidance and output/performance caveats. Every sentence earns its place; there is no filler or tautology. The structure makes it easy to scan and absorb.

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 tool's complexity (SQL over multiple tables, JSONB fields, export modes, potential for timeouts), the description is remarkably complete. It covers return value behavior (row cap, truncation, export links), performance pitfalls, schema resources to read first, and use-case boundaries. No output schema exists, but nothing is missing for selecting and invoking the tool correctly.

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

Parameters4/5

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

With 100% schema description coverage and highly detailed parameter descriptions, the baseline is 3. The tool description adds usage context for parameters: linking format=parquet|ndjson to avoiding truncation, and timeout_seconds to raising the wall-clock cap for long-running queries. This enriches the bare schema without being essential, so a 4 is appropriate rather than a 5.

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 clear verb+resource: 'run ONE read-only SQL statement' over specific tables ('sessions / messages / parts'). It explicitly distinguishes itself from siblings by stating 'NOT for finding or reading conversations' and naming pond_search and pond_get_session / pond_get_message as alternatives. This leaves no ambiguity about the tool's niche.

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

Usage Guidelines5/5

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

The description provides explicit when-to-use guidance: 'Reach for SQL only for: aggregation (counts, group-by, joins, time buckets), exact strings or identifiers in conversational text (contains_tokens / fts), tool-call analytics and tool bodies, subagent sessions, bulk export'. It also gives a clear exclusion ('NOT for finding or reading conversations') and names alternative tools, fulfilling the when/when-not/alternatives criteria perfectly.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 4 tool updatesv0.14.4
    • First observedpond_get_message
    • First observedpond_get_session
    • First observedpond_search
    • First observedpond_sql

TDQS

A4.7/5.0
Disambiguation5/5

Each tool targets a distinct purpose: pond_search for discovery, pond_get_message for single-message expansion, pond_get_session for full-session transcripts, and pond_sql for advanced SQL queries. The descriptions make the boundaries clear, and there is no overlap in functionality.

Naming Consistency4/5

All tools share the pond_ prefix and use snake_case, but the pattern is not perfectly uniform: two use get_ (pond_get_message, pond_get_session), one uses a bare verb (pond_search), and one uses a noun (pond_sql). This is mostly consistent but with a slight deviation.

Tool Count5/5

Four tools is well within the ideal 3-15 range for a focused server. Each tool earns its place: search, single-message read, session read, and SQL access cover the core recall/analytics functionality without redundancy.

Completeness5/5

The set provides a complete workflow for recalling past conversations: search to find, then either get a message or get a session to read, with SQL as an escape hatch for advanced queries, exact matching, and analytics. The documented gaps (tool bodies, subagent sessions) are explicitly addressed via SQL, so there are no dead ends.

Maintenance

ActivityActive
ResponsivenessWithin a week

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